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  • 1.
    Allalou, Amin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    van de Rijke, Frans
    Jahangir Tafrechi, Roos
    Raap, Anton
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Image based measurements of single cell mtDNA mutation load MTD 20072007In: Medicinteknikdagarna 2007, 2007Conference paper (Other (popular science, discussion, etc.))
    Abstract [en]

    Cell cultures as well as cells in tissue always display a certain degree of variability,and measurements based on cell averages will miss important information contained in a heterogeneous population. These differences among cells in a population may be essential to quantify when looking at, e.g., protein expression and mutations in tumor cells which often show high degree of heterogeneity.

    Single nucleotide mutations in the mithochondrial DNA (mtDNA) can accumulate and later be present in large proportions of the mithocondria causing devastating diseases. To study mtDNA accumulation and segregation one needs to measure the amount of mtDNA mutations in each cell in multiple serial cell culture passages. The different degrees of mutation in a cell culture can be quantified by making measurements on individual cells as an alternative to looking at an average of a population. Fluorescence microscopy in combination with automated digital image analysis provides an efficient approach to this type of single cell analysis.

    Image analysis software for these types of applications are often complicated and not easy to use for persons lacking extensive knowledge in image analysis, e.g., laboratory personnel. This paper presents a user friendly implementation of an automated method for image based measurements of mtDNA mutations in individual cells detected with padlock probes and rolling-circle amplification (RCA). The mitochondria are present in the cell’s cytoplasm, and here each cytoplasm has to be delineated without the presence of a cytoplasmic stain. Three different methods for segmentation of cytoplasms are compared and it is shown that automated cytoplasmic delineation can be performed 30 times faster than manual delineation, with an accuracy as high as 87%. The final image based measurements of mitochondrial mutation load are also compared to, and show high agreement with, measurements made using biochemical techniques.

  • 2.
    Allalou, Amin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    van de Rijke, Frans M.
    Jahangir Tafrechi, Roos
    Raap, Anton K.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Image Based Measurements of Single Cell mtDNA Mutation Load2007In: Image Analysis, Proceedings / [ed] Ersboll BK, Pedersen KS, 2007, p. 631-640Conference paper (Refereed)
    Abstract [en]

    Cell cultures as well as cells in tissue always display a certain degree of variability, and measurements based on cell averages will miss important information contained in a heterogeneous population. This paper presents automated methods for image based measurements of mitochondiral DNA (mtDNA) mutations in individual cells. The mitochondria are present in the cell’s cytoplasm, and each cytoplasm has to be delineated. Three different methods for segmentation of cytoplasms are compared and it is shown that automated cytoplasmic delineation can be performed 30 times faster than manual delineation, with an accuracy as high as 87%. The final image based measurements of mitochondrial mutation load are also compared to, and show high agreement with, measurements made using biochemical techniques.

  • 3.
    Allalou, Amin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    BlobFinder, a tool for fluorescence microscopy image cytometry2009In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 94, no 1, p. 58-65Article in journal (Refereed)
    Abstract [en]

    Images can be acquired at high rates with modern fluorescence microscopy hardware, giving rise to a demand for high-speed analysis of image data. Digital image cytometry, i.e., automated measurements and extraction of quantitative data from images of cells, provides valuable information for many types of biomedical analysis. There exists a number of different image analysis software packages that can be programmed to perform a wide array of useful measurements. However, the multi-application capability often compromises the simplicity of the tool. Also, the gain in speed of analysis is often compromised by time spent learning complicated software. We provide a free software called BlobFinder that is intended for a limited type of application, making it easy to use, easy to learn and optimized for its particular task. BlobFinder can perform batch processing of image data and quantify as well as localize cells and point like source signals in fluorescence microscopy images, e.g., from FISH, in situ PLA and padlock probing, in a fast and easy way.

    Download full text (pdf)
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  • 4.
    Allalou, Amin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Signal Detection in 3D by Stable Wave Signal Verification2009In: Proceedings of SSBA 2009, 2009Conference paper (Other academic)
    Abstract [en]

    Detection and localization of point-source signals is an important task in many image analysis applications. These types of signals can commonly be seen in fluorescent microscopy when studying functions of biomolecules. Visual detection and localization of point-source signals in 3D is limited and time consuming, making automated methods an important task. The 3D Stable Wave Detector (3DSWD) is a new method that combines signal enhancement with a verifier/separator. The verifier/separator examines the intensity gradient around a signal, making the detection less sensitive to noise and better at separating spatially close signals. Conventional methods such as; TopHat, Difference of Gaussian, and Multiscale Product consist only of signal enhancement. In this paper we compare the 3DSWD to these conventional methods with and without the addition of a verifier/separator. We can see that the 3DSWD has the highest robustness to noise among all the methods and that the other methods are improved when a verifier/separator is added.

  • 5.
    Allalou, Amin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    van de Rijke, Frans
    Jahangir Tafrechi, Roos
    Raap, Anton
    Segmentation of Cytoplasms of Cultured Cells2007In: In Proceedings SSBA 2007, Symposium on image analysis, Linköping, 2007Conference paper (Other academic)
    Abstract [en]

    Cell cultures as well as cells in tissue always display a certain degree of variability, and measurements based on cell averages will miss important information contained in a heterogeneous population. This paper presents automated methods for segmentation of cells and cytoplasms. The segmentation results are applied to image based measurements of mitochondiral DNA (mtDNA) mutations in individual cells. Three different methods for segmentation of cytoplasms are compared and it is shown that automated cytoplasmic delineation can be performed 30 times faster than manual delineation, with an accuracy as high as 87%, compared to an inter observer variability of 79% at manual delineation.

  • 6.
    Andersson, Axel
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Behanova, Andrea
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Avenel, Christophe
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Windhager, Jonas
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Malmberg, Filip
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Points2Regions: Fast, interactive clustering of imaging-based spatial transcriptomics dataManuscript (preprint) (Other academic)
    Abstract [en]

    Imaging-based spatial transcriptomics techniques generate image data that, once processed, results in a set of spatial points with categorical labels for different mRNA species. A crucial part of analyzing downstream data involves the analysis of these point patterns. Here, biologically interesting patterns can be explored at different spatial scales. Molecular patterns on a cellular level would correspond to cell types, whereas patterns on a millimeter scale would correspond to tissue-level structures. Often, clustering methods are employed to identify and segment regions with distinct point-patterns. Traditional clustering techniques for such data are constrained by reliance on complementary data or extensive machine learning, limiting their applicability to tasks on a particular scale. This paper introduces 'Points2Regions', a practical tool for clustering spatial points with categorical labels. Its flexible and computationally efficient clustering approach enables pattern discovery across multiple scales, making it a powerful tool for exploratory analysis. Points2Regions has demonstrated efficient performance in various datasets, adeptly defining biologically relevant regions similar to those found by scale-specific methods. As a Python package integrated into TissUUmaps and a Napari plugin, it offers interactive clustering and visualization, significantly enhancing user experience in data exploration. In essence, Points2Regions presents a user-friendly and simple tool for exploratory analysis of spatial points with categorical labels. 

  • 7.
    Andersson, Axel
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Behanova, Andrea
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Malmberg, Filip
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Cell Segmentation of in situ Transcriptomics Data using Signed Graph Partitioning2023In: Graph-Based Representations in Pattern Recognition / [ed] Mario Vento; Pasquale Foggia; Donatello Conte; Vincenzo Carletti, Cham: Springer, 2023, p. 139-148Conference paper (Refereed)
    Abstract [en]

    The locations of different mRNA molecules can be revealed by multiplexed in situ RNA detection. By assigning detected mRNA molecules to individual cells, it is possible to identify many different cell types in parallel. This in turn enables investigation of the spatial cellular architecture in tissue, which is crucial for furthering our understanding of biological processes and diseases. However, cell typing typically depends on the segmentation of cell nuclei, which is often done based on images of a DNA stain, such as DAPI. Limiting cell definition to a nuclear stain makes it fundamentally difficult to determine accurate cell borders, and thereby also difficult to assign mRNA molecules to the correct cell. As such, we have developed a computational tool that segments cells solely based on the local composition of mRNA molecules. First, a small neural network is trained to compute attractive and repulsive edges between pairs of mRNA molecules. The signed graph is then partitioned by a mutex watershed into components corresponding to different cells. We evaluated our method on two publicly available datasets and compared it against the current state-of-the-art and older baselines. We conclude that combining neural networks with combinatorial optimization is a promising approach for cell segmentation of in situ transcriptomics data. The tool is open-source and publicly available for use at https://github.com/wahlby-lab/IS3G.

  • 8.
    Andersson, Axel
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Diego, Ferran
    HCI/IWR and Department of Physics and Astronomy, Heidelberg University, Heidelberg.
    Hamprecht, Fred A.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. HCI/IWR and Department of Physics and Astronomy, Heidelberg University, Heidelberg.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    ISTDECO: In Situ Transcriptomics Decoding by DeconvolutionManuscript (preprint) (Other academic)
    Abstract [en]

    In Situ Transcriptomics (IST) is a set of image-based transcriptomics approaches that enables localisation of gene expression directly in tissue samples. IST techniques produce multiplexed image series in which fluorescent spots are either present or absent across imaging rounds and colour channels. A spot’spresence and absence form a type of barcoded pattern that labels a particular type of mRNA. Therefore, the expression of agene can be determined by localising the fluorescent spots and decode the barcode that they form. Existing IST algorithms usually do this in two separate steps: spot localisation and barcode decoding. Although these algorithms are efficient, they are limited by strictly separating the localisation and decoding steps. This limitation becomes apparent in regions with low signal-to-noise ratio or high spot densities. We argue that an improved gene expression decoding can be obtained by combining these two steps into a single algorithm. This allows for an efficient decoding that is less sensitive to noise and optical crowding. We present IST Decoding by Deconvolution (ISTDECO), a principled decoding approach combining spectral and spatial deconvolution into a single algorithm. We evaluate ISTDECOon simulated data, as well as on two real IST datasets, and compare with state-of-the-art. ISTDECO achieves state-of-the-art performance despite high spot densities and low signal-to-noise ratios. It is easily implemented and runs efficiently using a GPU.ISTDECO implementation, datasets and demos are available online at: github.com/axanderssonuu/istdeco

  • 9.
    Andersson, Axel
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Escriva Conde, Maria
    Stockholm University.
    Surova, Olga
    Stockholm University.
    Vermeulen, Peter
    GZA Hospital Sint-Augustinus.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Nilsson, Mats
    Stockholm University.
    Nyström, Hanna
    Umeå University.
    Spatial transcriptome mapping of the desmoplastic growth pattern of colorectal liver metastases by in situ sequencing reveals a biologically relevant zonation of the desmoplastic rimManuscript (preprint) (Other academic)
  • 10.
    Andersson, Axel
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Partel, Gabriele
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Solorzano, Leslie
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Wählby, Carolina
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Transcriptome-Supervised Classification of Tissue Morphology Using Deep Learning2020In: IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020, p. 1630-1633Conference paper (Refereed)
    Abstract [en]

    Deep learning has proven to successfully learn variations in tissue and cell morphology. Training of such models typically relies on expensive manual annotations. Here we conjecture that spatially resolved gene expression, e.i., the transcriptome, can be used as an alternative to manual annotations. In particular, we trained five convolutional neural networks with patches of different size extracted from locations defined by spatially resolved gene expression. The network is trained to classify tissue morphology related to two different genes, general tissue, as well as background, on an image of fluorescence stained nuclei in a mouse brain coronal section. Performance is evaluated on an independent tissue section from a different mouse brain, reaching an average Dice score of 0.51. Results may indicate that novel techniques for spatially resolved transcriptomics together with deep learning may provide a unique and unbiased way to find genotype phenotype relationships

  • 11.
    Beháňová, Andrea
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Avenel, Christophe
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Andersson, Axel
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Chelebian, Eduard
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Klemm, Anna
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Wik, Lina
    Östman, Arne
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Visualization and quality control tools for large-scale multiplex tissue analysis in TissUUmaps32023In: Biological Imaging, E-ISSN 2633-903X, Vol. 3, article id e6Article in journal (Refereed)
  • 12.
    Beháňová, Andrea
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Klemm, Anna H
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wählby, Carolina
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Spatial Statistics for Understanding Tissue Organization2022In: Frontiers in Physiology, E-ISSN 1664-042X, Vol. 13, article id 832417Article, review/survey (Refereed)
    Abstract [en]

    Interpreting tissue architecture plays an important role in gaining a better understanding of healthy tissue development and disease. Novel molecular detection and imaging techniques make it possible to locate many different types of objects, such as cells and/or mRNAs, and map their location across the tissue space. In this review, we present several methods that provide quantification and statistical verification of observed patterns in the tissue architecture. We categorize these methods into three main groups: Spatial statistics on a single type of object, two types of objects, and multiple types of objects. We discuss the methods in relation to four hypotheses regarding the methods' capability to distinguish random and non-random distributions of objects across a tissue sample, and present a number of openly available tools where these methods are provided. We also discuss other spatial statistics methods compatible with other types of input data.

    Download full text (pdf)
    FULLTEXT01
  • 13.
    Bekkhus, Tove
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.
    Avenel, Christophe
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Hanna, Sabella
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.
    Franzén Boger, Mathias
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Klemm, Anna H
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Vasiliu-Bacovia, Daniel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Wärnberg, Fredrik
    Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Ulvmar, Maria H.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.
    Automated detection of vascular remodeling in human tumor draining lymph nodes by the deep learning tool HEV-finder2022In: Journal of Pathology, ISSN 0022-3417, E-ISSN 1096-9896, Vol. 258, no 1, p. 4-11Article in journal (Refereed)
    Abstract [en]

    Vascular remodeling is common in human cancer and has potential as future biomarkers for prediction of disease progression and tumor immunity status. It can also affect metastatic sites, including the tumor-draining lymph nodes (TDLNs). Dilation of the high endothelial venules (HEVs) within TDLNs has been observed in several types of cancer. We recently demonstrated that it is a premetastatic effect that can be linked to tumor invasiveness in breast cancer. Manual visual assessment of changes in vascular morphology is a tedious and difficult task, limiting high-throughput analysis. Here we present a fully automated approach for detection and classification of HEV dilation. By using 12,524 manually classified HEVs, we trained a deep-learning model and created a graphical user interface for visualization of the results. The tool, named the HEV-finder, selectively analyses HEV dilation in specific regions of the lymph nodes. We evaluated the HEV-finder's ability to detect and classify HEV dilation in different types of breast cancer compared to manual annotations. Our results constitute a successful example of large-scale, fully automated, and user-independent, image-based quantitative assessment of vascular remodeling in human pathology and lay the ground for future exploration of HEV dilation in TDLNs as a biomarker.

    Download full text (pdf)
    fulltext
  • 14.
    Bengtsson, Ewert
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala university.
    Wieslander, Håkan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Forslid, Gustav
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Hirsch, Jan-Michael
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Oral and Maxillofacial Surgery.
    Runow Stark, Christina
    Kecheril Sadanandan, Sajith
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Detection of Malignancy-Associated Changes Due to Precancerous and Oral Cancer Lesions: A Pilot Study Using Deep Learning2018In: CYTO2018 / [ed] Andrea Cossarizza, 2018Conference paper (Refereed)
    Abstract [en]

    Background: The incidence of oral cancer is increasing and it is effecting younger individuals. PAP smear-based screening, visual, and automated, have been used for decades, to successfully decrease the incidence of cervical cancer. Can similar methods be used for oral cancer screening? We have carried out a pilot study using neural networks for classifying cells, both from cervical cancer and oral cancer patients. The results which were reported from a technical point of view at the 2017 IEEE International Conference on Computer Vision Workshop (ICCVW), were particularly interesting for the oral cancer cases, and we are currently collecting and analyzing samples from more patients. Methods: Samples were collected with a brush in the oral cavity and smeared on glass slides, stained, and prepared, according to standard PAP procedures. Images from the slides were digitized with a 0.35 micron pixel size, using focus stacks with 15 levels 0.4 micron apart. Between 245 and 2,123 cell nuclei were manually selected for analysis for each of 14 datasets, usually 2 datasets for each of the 6 cases, in total around 15,000 cells. A small region was cropped around each nucleus, and the best 2 adjacent focus layers in each direction were automatically found, thus creating images of 100x100x5 pixels. Nuclei were chosen with an aim to select well preserved free-lying cells, with no effort to specifically select diagnostic cells. We therefore had no ground truth on the cellular level, only on the patient level. Subsets of these images were used for training 2 sets of neural networks, created according to the ResNet and VGG architectures described in literature, to distinguish between cells from healthy persons, and those with precancerous lesions. The datasets were augmented through mirroring and 90 degrees rotations. The resulting networks were used to classify subsets of cells from different persons, than those in the training sets. This was repeated for a total of 5 folds. Results: The results were expressed as the percentage of cell nuclei that the neural networks indicated as positive. The percentage of positive cells from healthy persons was in the range 8% to 38%. The percentage of positive cells collected near the lesions was in the range 31% to 96%. The percentages from the healthy side of the oral cavity of patients with lesions ranged 37% to 89%. For each fold, it was possible to find a threshold for the number of positive cells that would correctly classify all patients as normal or positive, even for the samples taken from the healthy side of the oral cavity. The network based on the ResNet architecture showed slightly better performance than the VGG-based one. Conclusion: Our small pilot study indicates that malignancyassociated changes that can be detected by neural networks may exist among cells in the oral cavity of patients with precancerous lesions. We are currently collecting samples from more patients, and will present those results as well, with our poster at CYTO 2018.

  • 15.
    Bengtsson, Ewert
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Robust cell image segmentation methods2004In: Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, ISSN 1054-6618, Vol. 14, no 2, p. 157-167Article in journal (Refereed)
    Abstract [en]

    Biomedical cell image analysis is one of the main application fields of computerized image analysis. This paper outlines the field and the different analysis steps related to it. Relative advantages of different approaches to the crucial step of image segmentation are discussed. Cell image segmentation can be seen as a modeling problem where different approaches are more or less explicitly based on cell models. For example, thresholding methods can be seen as being based on a model stating that cells have an intensity that is different from the surroundings. More robust segmentation can be obtained if a combination of features, such as intensity, edge gradients, and cellular shape, is used. The seeded watershed transform is proposed as the most useful tool for incorporating such features into the cell model. These concepts are illustrated by three real-world problems.

  • 16.
    Blamey, Ben
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Toor, Salman
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
    Dahlö, Martin
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wieslander, Håkan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Harrison, Philip J.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Vironova AB.
    Sabirsh, Alan
    AstraZeneca.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Hellander, Andreas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit2021In: GigaScience, E-ISSN 2047-217X, Vol. 10, no 3, p. 1-14, article id giab018Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Large streamed datasets, characteristic of life science applications, are often resource-intensive to process, transport and store. We propose a pipeline model, a design pattern for scientific pipelines, where an incoming stream of scientific data is organized into a tiered or ordered "data hierarchy". We introduce the HASTE Toolkit, a proof-of-concept cloud-native software toolkit based on this pipeline model, to partition and prioritize data streams to optimize use of limited computing resources.

    FINDINGS: In our pipeline model, an "interestingness function" assigns an interestingness score to data objects in the stream, inducing a data hierarchy. From this score, a "policy" guides decisions on how to prioritize computational resource use for a given object. The HASTE Toolkit is a collection of tools to adopt this approach. We evaluate with 2 microscopy imaging case studies. The first is a high content screening experiment, where images are analyzed in an on-premise container cloud to prioritize storage and subsequent computation. The second considers edge processing of images for upload into the public cloud for real-time control of a transmission electron microscope.

    CONCLUSIONS: Through our evaluation, we created smart data pipelines capable of effective use of storage, compute, and network resources, enabling more efficient data-intensive experiments. We note a beneficial separation between scientific concerns of data priority, and the implementation of this behaviour for different resources in different deployment contexts. The toolkit allows intelligent prioritization to be `bolted on' to new and existing systems - and is intended for use with a range of technologies in different deployment scenarios.

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    fulltext
  • 17.
    Bombrun, Maxime
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Gao, Hui
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Mejhert, Niklas
    Arner, Peter
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Quantitative high-content/high-throughput microscopy analysis of lipid droplets in subject-specific adipogenesis models2017In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 91, no 11, p. 1068-1077Article in journal (Refereed)
  • 18.
    Bombrun, Maxime
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Allalou, Amin
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Partel, Gabriele
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Solorzano, Leslie
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Qian, Xiaoyan
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Tomtebodavagen 23, S-17165 Solna, Sweden.
    Nilsson, Mats
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Tomtebodavagen 23, S-17165 Solna, Sweden.
    Wählby, Carolina
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Decoding gene expression in 2D and 3D2017In: Image Analysis: Part II, Springer, 2017, p. 257-268Conference paper (Refereed)
    Abstract [en]

    Image-based sequencing of RNA molecules directly in tissue samples provides a unique way of relating spatially varying gene expression to tissue morphology. Despite the fact that tissue samples are typically cut in micrometer thin sections, modern molecular detection methods result in signals so densely packed that optical “slicing” by imaging at multiple focal planes becomes necessary to image all signals. Chromatic aberration, signal crosstalk and low signal to noise ratio further complicates the analysis of multiple sequences in parallel. Here a previous 2D analysis approach for image-based gene decoding was used to show how signal count as well as signal precision is increased when analyzing the data in 3D instead. We corrected the extracted signal measurements for signal crosstalk, and improved the results of both 2D and 3D analysis. We applied our methodologies on a tissue sample imaged in six fluorescent channels during five cycles and seven focal planes, resulting in 210 images. Our methods are able to detect more than 5000 signals representing 140 different expressed genes analyzed and decoded in parallel.

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  • 19.
    Bombrun, Maxime
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    A web application to analyse and visualize digital images at multiple resolutions2017Conference paper (Other academic)
    Abstract [en]

    Computerised image processing and automated quantification of cell and tissue morphology are becoming important tools for complementing visual assessment when investigating disease and/or drug response. The distribution and organisation of cells in intact tissue samples provides a rich visual-cognitive combination of information at multiple resolutions. The lowest magnification describes specific architectural patterns in the global tissue organization. At the same time, new methods for in situ sequencing of RNA allows profiling of gene expression at cellular resolution. Analysis at multiple resolutions thus opens up for large-scale comparison of genotype and phenotype. Expressed genes are locally amplified by molecular probes and rolling circle amplification, and decoded by repeating the sequencing cycle for the four letters of the genetic code. Using image processing methodologies on these giga-pixel images (40000 x 48000 pixels), we have identified more than 40 genes in parallel in the same tissue sample. Here, we present an open-source tool which combines the quantification of cell and tissue morphology with the analysis of gene expression. Our framework builds on CellProfiler, a free and open-source software developed for image based screening, and our viewing platform allow experts to visualize both gene expression patterns and quantitative measurements of tissue morphology with different overlays, such as the commonly used H&E staining. Furthermore, the user can draw regions of interest and extract local statistics on gene expression and tissue morphology over large slide scanner images at different resolutions. The TissueMaps platform provides a flexible solution to support the future development of histopathology, both as a diagnostic tool and as a research field.

  • 20.
    Bombrun, Maxime
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    TissueMaps: A large multi-scale data analysis platform for digital image application built on open-source software2016Conference paper (Other academic)
    Abstract [en]

    Automated analysis of microscopy data and quantification of cell and tissue morphology has become an important tool for investigating disease and/or drug response. New methods of in situ sequencing of RNA allows profiling of gene expression at cellular resolution in intact tissue samples, and thus opens up for large-scale comparison of genotype and phenotype. Expressed genes are locally amplified by molecular probes and rolling circle amplification, and decoded by analysis of repeated imaging and sequencing cycles. Using image processing methodologies on these giga-pixel images (40000 x 48000 pixels), we have identified more than 40 genes in parallel in the same tissue sample. On the other hand, the distribution and organisation of cells in the tissue contain rich information at multiple resolutions. The lowest resolution describes the global tissue arrangement, while the cellular resolution allows us to quantify gene expression and morphology of individual cells.

    Here, we present an open-source tool which combine the analysis of gene expression with quantification of cell and tissue morphology. Our framework builds on CellProfiler, a free and open-source software developed for image based screening, and our viewing platform allow experts to visualize analysis results with different overlays, such as the commonly used H&E staining. Furthermore, the user can draw regions of interest and extract local statistics on gene expression and tissue morphology over large slide scanner images at different resolutions (Fig.1). The TissueMaps platform provides a flexible solution to support the future development of histopathology, both as a diagnostic tool and as a research field.

  • 21.
    Chang, Tsung-Yao
    et al.
    Massachusetts Institute of Technology, USA.
    Pardo-Martin, Carlos
    Massachusetts Institute of Technology, USA.
    Allalou, Amin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Yanik, Mehmet Fatih
    Massachusetts Institute of Technology, USA.
    Fully automated cellular-resolution vertebrate screening platform with parallel animal processing2012In: Lab on a Chip, ISSN 1473-0197, E-ISSN 1473-0189, Vol. 12, no 4, p. 711-716Article in journal (Refereed)
    Abstract [en]

    The zebrafish larva is an optically-transparent vertebrate model with complex organs that is widelyused to study genetics, developmental biology, and to model various human diseases. In this article, wepresent a set of novel technologies that significantly increase the throughput and capabilities of ourpreviously described vertebrate automated screening technology (VAST). We developed a robustmulti-thread system that can simultaneously process multiple animals. System throughput is limitedonly by the image acquisition speed rather than by the fluidic or mechanical processes. We developedimage recognition algorithms that fully automate manipulation of animals, including orienting andpositioning regions of interest within the microscope’s field of view. We also identified the optimalcapillary materials for high-resolution, distortion-free, low-background imaging of zebrafish larvae.

  • 22.
    Chelebian, Eduard
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Avenel, Christophe
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Kartasalo, Kimmo
    Marklund, Maja
    Tanoglidi, Anna
    Uppsala Univ Hosp, Dept Clin Pathol, S-75237 Uppsala, Sweden..
    Mirtti, Tuomas
    Colling, Richard
    Erickson, Andrew
    Lamb, Alastair D.
    Lundeberg, Joakim
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer2021In: Cancers, ISSN 2072-6694, ISSN 2072-6694, Vol. 13, no 19, article id 4837Article in journal (Refereed)
    Abstract [en]

    Prostate cancer is a common cancer type in men, yet some of its traits are still under-explored. One reason for this is high molecular and morphological heterogeneity. The purpose of this study was to develop a method to gain new insights into the connection between morphological changes and underlying molecular patterns. We used artificial intelligence (AI) to analyze the morphology of seven hematoxylin and eosin (H & E)-stained prostatectomy slides from a patient with multi-focal prostate cancer. We also paired the slides with spatially resolved expression for thousands of genes obtained by a novel spatial transcriptomics (ST) technique. As both spaces are highly dimensional, we focused on dimensionality reduction before seeking associations between them. Consequently, we extracted morphological features from H & E images using an ensemble of pre-trained convolutional neural networks and proposed a workflow for dimensionality reduction. To summarize the ST data into genetic profiles, we used a previously proposed factor analysis. We found that the regions were automatically defined, outlined by unsupervised clustering, associated with independent manual annotations, in some cases, finding further relevant subdivisions. The morphological patterns were also correlated with molecular profiles and could predict the spatial variation of individual genes. This novel approach enables flexible unsupervised studies relating morphological and genetic heterogeneity using AI to be carried out.

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  • 23.
    Clausson, Carl-Magnus
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Allalou, Amin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Weibrecht, Irene
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Mahmoudi, Salah
    Farnebo, Marianne
    Landegren, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Söderberg, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Increasing the dynamic range of in situ PLA2011In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 8, no 11, p. 892-893Article in journal (Refereed)
  • 24.
    Clausson, Carl-Magnus
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools.
    Arngården, Linda
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools.
    Ishaq, Omer
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Klaesson, Axel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools.
    Kühnemund, Malte
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools.
    Grannas, Karin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools.
    Koos, Björn
    Qian, Xiaoyan
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Krzywkowski, Tomasz
    Brismar, Hjalmar
    Nilsson, Mats
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Söderberg, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools.
    Compaction of rolling circle amplification products increases signal integrity and signal–to–noise ratio2015In: Scientific Reports, E-ISSN 2045-2322, Vol. 5, p. 12317:1-10, article id 12317Article in journal (Refereed)
  • 25.
    Clausson, Carl-Magnus
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Söderberg, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Arngården, Linda
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Ishaq, Omer
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Nilsson, Mats
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Krzywkowski, Tomasz
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Compaction of rolling circle amplification products increases signal strength and integrityManuscript (preprint) (Other academic)
  • 26. Degerman, Johan
    et al.
    Althoff, Karin
    Thorlin, Thorleif
    Wählby, Carolina
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Karlsson, Patrick
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Gustavsson, Tomas
    Modeling stem cell migration by Hidden Markov2004In: Proceedings of the Swedish Symposium on Image Analysis, SSBA 2004, 2004, p. 122-125Conference paper (Other scientific)
  • 27.
    Dobson, Ellen T.A.
    et al.
    Laboratory for Optical and Computational Instrumentation (LOCI) Center for Quantitative Cell Imaging University of Wisconsin at Madison Madison Wisconsin.
    Cimini, Beth
    Imaging Platform Broad Institute of Harvard and MIT Cambridge Massachusetts.
    Klemm, Anna H
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Wählby, Carolina
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Carpenter, Anne E.
    Imaging Platform Broad Institute of Harvard and MIT Cambridge Massachusetts.
    Eliceiri, Kevin W
    Laboratory for Optical and Computational Instrumentation (LOCI) Center for Quantitative Cell Imaging University of Wisconsin at Madison Madison Wisconsin;Department of Medical Physics University of Wisconsin at Madison Madison Wisconsin;Morgridge Institute for Research Madison Wisconsin.
    ImageJ and CellProfiler: Complements in Open‐Source Bioimage Analysis2021In: Current Protocols in Microbiology, ISSN 1934-8525, E-ISSN 1088-7423, Vol. 1, no 5Article in journal (Refereed)
    Abstract [en]

    ImageJ and CellProfiler have long been leading open-source platforms in the field of bioimage analysis. ImageJ's traditional strength is in single-image processing and investigation, while CellProfiler is designed for building large-scale, modular analysis pipelines. Although many image analysis problems can be well solved with one or the other, using these two platforms together in a single workflow can be powerful. Here, we share two pipelines demonstrating mechanisms for productively and conveniently integrating ImageJ and CellProfiler for (1) studying cell morphology and migration via tracking, and (2) advanced stitching techniques for handling large, tiled image sets to improve segmentation. No single platform can provide all the key and most efficient functionality needed for all studies. While both programs can be and are often used separately, these pipelines demonstrate the benefits of using them together for image analysis workflows. ImageJ and CellProfiler are both committed to interoperability between their platforms, with ongoing development to improve how both are leveraged from the other

  • 28.
    Edfeldt, Gabriella
    et al.
    Karolinska Inst, Stockholm, Sweden.
    Lajoie, Julie
    Univ Manitoba, Winnipeg, MB, Canada.
    Röhl, Maria
    Karolinska Inst, Stockholm, Sweden.
    Omollo, Kenneth
    Univ Nairobi, Nairobi, Kenya.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Boily-Larouche, Genevieve
    Univ Manitoba, Winnipeg, MB, Canada.
    Kimani, Joshua
    Univ Nairobi, Nairobi, Kenya.
    Fowke, Keith
    Univ Manitoba, Winnipeg, MB, Canada; Univ Nairobi, Nairobi, Kenya.
    Broliden, Kristina
    Karolinska Inst, Stockholm, Sweden.
    Tjernlund, Annelie
    Karolinska Inst, Stockholm, Sweden.
    The Effect of DMPA Use on the Human Cervical Epithelium: Mechanisms Revealed by Image Analysis2018In: AIDS Research and Human Retroviruses, ISSN 0889-2229, E-ISSN 1931-8405, Vol. 34, no S1, p. 310-310Article in journal (Other academic)
  • 29. Edfeldt, Gabriella
    et al.
    Lajoie, Julie
    Röhl, Maria
    Oyugi, Julius
    Åhlberg, Alexandra
    Khalilzadeh-Binicy, Behnaz
    Bradley, Frideborg
    Mack, Mathias
    Kimani, Joshua
    Omollo, Kenneth
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Fowke, Keith R.
    Broliden, Kristina
    Tjernlund, Annelie
    Regular Use of Depot Medroxyprogesterone Acetate Causes Thinning of the Superficial Lining and Apical Distribution of Human Immunodeficiency Virus Target Cells in the Human Ectocervix2022In: Journal of Infectious Diseases, ISSN 0022-1899, E-ISSN 1537-6613, Vol. 225, no 7, p. 1151-1161Article in journal (Refereed)
    Abstract [en]

    Background

    The hormonal contraceptive depot medroxyprogesterone acetate (DMPA) may be associated with an increased risk of acquiring human immunodeficiency virus (HIV). We hypothesize that DMPA use influences the ectocervical tissue architecture and HIV target cell localization.

    Methods

    Quantitative image analysis workflows were developed to assess ectocervical tissue samples collected from DMPA users and control subjects not using hormonal contraception.

    Results

    Compared to controls, the DMPA group exhibited a significantly thinner apical ectocervical epithelial layer and a higher proportion of CD4+CCR5+ cells with a more superficial location. This localization corresponded to an area with a nonintact E-cadherin net structure. CD4+Langerin+ cells were also more superficially located in the DMPA group, although fewer in number compared to the controls. Natural plasma progesterone levels did not correlate with any of these parameters, whereas estradiol levels were positively correlated with E-cadherin expression and a more basal location for HIV target cells of the control group.

    Conclusions

    DMPA users have a less robust epithelial layer and a more apical distribution of HIV target cells in the human ectocervix, which could confer a higher risk of HIV infection. Our results highlight the importance of assessing intact genital tissue samples to gain insights into HIV susceptibility factors.

    Download full text (pdf)
    fulltext
  • 30. Edfeldt, Gabriella
    et al.
    Lajoie, Julie
    Röhl, Maria
    Tjernlund, Annelie
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Omollo, Kenneth Odiwuor
    Boily-Larouche, Genevieve
    Cheruiyot, Julianna
    Kimani, Makubo
    Kimani, Joshua
    Oyugi, Julius
    Fowke, Keith R.
    Broliden, Kristina
    Hormonal contraceptive use affects HIV susceptibility: mechanisms revealed by image analysis2017In: Scandinavian Journal of Immunology, ISSN 0300-9475, E-ISSN 1365-3083, Vol. 86, no 4, p. 281-281Article in journal (Other academic)
  • 31. Erlandsson, Fredrik
    et al.
    Wählby, Carolina
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Zetterberg, Anders
    University Administration. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Detection of large numbers of antigens using sequential immunofluorescence staining2001In: 7th European Society for Analytical Cellular Pathology Congress (ESACP 2001), Caen, France, 2001, p. 56-57Conference paper (Other scientific)
  • 32.
    Erlandsson, Fredrik
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Ekholm-Reed, Susanna
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Hellström, Ann-Cathrin
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Zetterberg, Anders
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Abnormal expression pattern of cyclin E in tumour cells2003In: Int J Cancer, ISSN 0020-7136, Vol. 104, p. 369-375Article in journal (Refereed)
  • 33.
    Erlandsson, Fredrik
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby (nee Linnman), Carolina
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Ekholm, Susanna
    Bengtsson, Ewert
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Zetterberg, Anders
    A detailed analysis of cyclin A accumulation at the G1/S border in normal and transformed cells.2000In: Experimental Cell Research, ISSN 0014-4827/00, Vol. 256, p. 86-95Article in journal (Refereed)
    Abstract [en]

    Automatic cell segmentation has various applications in cytometry, and while the

    nucleus is often very distinct and easy to identify, the cytoplasm provides a lot

    more challenge. A new combination of image analysis algorithms for

    segmentation of cells imaged by fluorescence microscopy is presented. The

    algorithm consists of an image pre-processing step, a general segmentation

    and merging step followed by a segmentation quality measurement. The quality

    measurement consists of a statistical analysis of a number of shape descriptive

    features. Objects that have features that differ to that of correctly segmented

    single cells can be further processed by a splitting step. By statistical analysis

    we therefore get a feedback system for separation of clustered cells. After the

    segmentation is completed, the quality of the final segmentation is evaluated. By

    training the algorithm on a representative set of training images, the algorithm

    is made fully automatic for subsequent images created under similar conditions.

    Automatic cytoplasm segmentation was tested on CHO-cells stained with

    calcein. The fully automatic method showed between 89% and 97% correct

    segmentation as compared to manual segmentation.

  • 34.
    Gavrilovic, Milan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Azar, Jimmy
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Busch, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology.
    Carlbom, Ingrid
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Blind Color Decomposition of Histological Images2013In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 32, no 6, p. 983-994Article in journal (Refereed)
    Abstract [en]

    Cancer diagnosis is based on visual examination under a microscope of tissue sections from biopsies. But whereas pathologists rely on tissue stains to identify morphological features, automated tissue recognition using color is fraught with problems that stem from image intensity variations due to variations in tissue preparation, variations in spectral signatures of the stained tissue, spectral overlap and spatial aliasing in acquisition, and noise at image acquisition. We present a blind method for color decomposition of histological images. The method decouples intensity from color information and bases the decomposition only on the tissue absorption characteristics of each stain. By modeling the charge-coupled device sensor noise, we improve the method accuracy. We extend current linear decomposition methods to include stained tissues where one spectral signature cannot be separated from all combinations of the other tissues' spectral signatures. We demonstrate both qualitatively and quantitatively that our method results in more accurate decompositions than methods based on non-negative matrix factorization and independent component analysis. The result is one density map for each stained tissue type that classifies portions of pixels into the correct stained tissue allowing accurate identification of morphological features that may be linked to cancer.

  • 35.
    Gavrilovic, Milan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Weibrecht, Irene
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Conze, Tim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Söderberg, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Automated Classification of Multicolored Rolling Circle Products in Dual-Channel Wide-Field Fluorescence Microscopy2011In: Cytometry Part A, ISSN 1552-4922, Vol. 79A, no 7, p. 518-527Article in journal (Refereed)
    Abstract [en]

    Specific single-molecule detection opens new possibilities in genomics and proteomics, and automated image analysis is needed for accurate quantification. This work presents image analysis methods for the detection and classification of single molecules and single-molecule interactions detected using padlock probes or proximity ligation. We use simple, widespread, and cost-efficient wide-field microscopy and increase detection multiplexity by labeling detection events with combinations of fluorescence dyes. The mathematical model presented herein can classify the resulting point-like signals in dual-channel images by spectral angles without discriminating between low and high intensity. We evaluate the methods on experiments with known signal classes and compare to classical classification algorithms based on intensity thresholding. We also demonstrate how the methods can be used as tools to evaluate biochemical protocols by measuring detection probe quality and accuracy. Finally, the method is used to evaluate single-molecule detection events in situ.

  • 36.
    Gavrilovic, Milan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Algorithms for cross-talk suppression in fluorescence microscopy2008In: Medicinteknikdagarna 2008, 2008, p. 64-64Conference paper (Other academic)
    Abstract [en]

     

     

     

    Download full text (pdf)
    FULLTEXT01
  • 37.
    Gavrilovic, Milan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Spectral Angle Histogram: a Novel Image Analysis Tool for Quantification of Colocalization and Cross-talk2009In: 9th International ELMI Meeting on Advanced Light Microscopy / [ed] Kurt Anderson, Gail McConnell, Glasgow, UK, 2009, p. 66-67Conference paper (Other academic)
    Abstract [en]

    In fluorescence microscopy, when analyzing spectral components, it is common to record two (or more) greyscale images. Each greyscale image, referred to as a channel, corresponds to intensities in different wavelength intervals. If each pixel of a two-channel image is plotted in a space spanned by the two intensity channels a conventional scatter-plot is obtained. Single-coloured pixels are distributed along the axes, while colocalized pixels are distributed closer to the diagonal of the scatter-plot, and cross-talk (as well as noise) is observed as deviations of the single-coloured vectors from the axes. Detection of colocalized pixels is often based on a division of this 2D space into different regions by intensity thresholding. We have developed a method for reducing the scatter-plot to a 1D spectral angle histogram through a series of steps that compensate for the quantization noise which is always present in digital image data.

    Using the spectral angle histogram, we can quantify colocalization in a fully automated and robust manner. As compared to previous methods for quantification of colocalization, this approach is insensitive to cross-talk. In fact, it can also be employed to quantify and compensate for cross-talk, using either linear unmixing or fuzzy classification by spectral angle, ensuring complete suppression of cross-talk with minimal loss of information. Recently we started investigating how the method can deal with autofluorescence. Initial tests on real image data show that the method may be useful for improved background suppression and amplification of the true signals.

    The article “Quantification of colocalization and cross-talk based on spectral angles”, describing the method, is about to be published in the Journal of Microscopy. Authors have also filed a patent application “Pixel classification in image analysis” in 2008.

    Download full text (txt)
    ELMIabstract
  • 38. Gibbs, Anna
    et al.
    Buggert, Marcus
    Edfeldt, Gabriella
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Introini, Andrea
    Cheuk, Stanley
    Martini, Elisa
    Eidsmo, Liv
    Ball, Terry B.
    Kimani, Joshua
    Kaul, Rupert
    Karlsson, Annika C.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Broliden, Kristina
    Tjernlund, Annelie
    Human Immunodeficiency Virus-Infected Women Have High Numbers of CD103-CD8+ T Cells Residing Close to the Basal Membrane of the Ectocervical Epithelium2018In: Journal of Infectious Diseases, ISSN 0022-1899, E-ISSN 1537-6613, Vol. 218, no 3, p. 453-465Article in journal (Refereed)
  • 39. Gibbs, Anna
    et al.
    Buggert, Marcus
    Edfeldt, Gabriella
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Introini, Andrea
    Cheuk, Stanley
    Martini, Elisa
    Eidsmo, Liv
    Hirbod, Taha
    Ball, Terry B.
    Kimani, Joshua
    Kaul, Rupert
    Karlsson, Annika C.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Broliden, Kristina
    Tjernlund, Annelie
    Increased numbers of CD103-CD8+ TRM cells in the cervical mucosa of HIV-infected women2017In: Scandinavian Journal of Immunology, ISSN 0300-9475, E-ISSN 1365-3083, Vol. 86, no 4, p. 288-289Article in journal (Other academic)
  • 40. Gibbs, Anna
    et al.
    Buggert, Marcus
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Introini, Andrea
    Cheuk, Stanley
    Eidsmo, Liv
    Hirbod, Taha
    Ball, Terry B.
    Kimani, Joshua
    Kaul, Rupert
    Karlsson, Annika C.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Broliden, Kristina
    Tjernlund, Annelie
    Analysis of the distribution of CD103 on CD8 T cells in blood and genital mucosa of HIV-infected female sex workers2016In: AIDS Research and Human Retroviruses, ISSN 0889-2229, E-ISSN 1931-8405, Vol. 32, no S1, p. 307-307Article in journal (Refereed)
  • 41.
    Gupta, Anindya
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Harrison, Philip J.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wieslander, Håkan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Pielawski, Nicolas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Kartasalo, Kimmo
    Partel, Gabriele
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Solorzano, Leslie
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Suveer, Amit
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Klemm, Anna H.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Deep Learning in Image Cytometry: A Review2019In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 95, no 6, p. 366-380Article, review/survey (Refereed)
    Download full text (pdf)
    fulltext
  • 42.
    Gupta, Anindya
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Larsson, Veronica
    Karolinska Inst, Dept Biosci & Nutr, Huddinge, Sweden.
    Matuszewski, Damian J.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Strömblad, Staffan
    Karolinska Inst, Dept Biosci & Nutr, Huddinge, Sweden.
    Wählby, Carolina
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Weakly-supervised prediction of cell migration modes in confocal microscopy images using bayesian deep learning2020In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020, p. 1626-1629Conference paper (Refereed)
    Abstract [en]

    Cell migration is pivotal for their development, physiology and disease treatment. A single cell on a 2D surface can utilize continuous or discontinuous migration modes. To comprehend the cell migration, an adequate quantification for single cell-based analysis is crucial. An automatized approach could alleviate tedious manual analysis, facilitating large-scale drug screening. Supervised deep learning has shown promising outcomes in computerized microscopy image analysis. However, their implication is limited due to the scarcity of carefully annotated data and uncertain deterministic outputs. We compare three deep learning models to study the problem of learning discriminative morphological representations using weakly annotated data for predicting the cell migration modes. We also estimate Bayesian uncertainty to describe the confidence of the probabilistic predictions. Amongst three compared models, DenseNet yielded the best results with a sensitivity of 87.91%±13.22 at a false negative rate of 1.26%±4.18.

  • 43.
    Gupta, Ankit
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Harrison, Philip J
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wieslander, Håkan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Rietdijk, Jonne
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Carreras-Puigvert, Jordi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Georgiev, Polina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Is brightfield all you need for MoA prediction?2022Conference paper (Refereed)
    Abstract [en]

    Fluorescence staining techniques, such as Cell Painting, together with fluorescence microscopy have proven invaluable for visualizing and quantifying the effects that drugs and other perturbations have on cultured cells. However, fluorescence microscopy is expensive, time-consuming, and labor-intensive, and the stains applied can be cytotoxic, interfering with the activity under study. The simplest form of microscopy, brightfield microscopy, lacks these downsides, but the images produced have low contrast and the cellular compartments are difficult to discern. Nevertheless, by harnessing deep learning, these brightfield images may still be sufficient for various predictive purposes. In this study, we compared the predictive performance of models trained on fluorescence images to those trained on brightfield images for predicting the mechanism of action (MoA) of different drugs. We also extracted CellProfiler features from the fluorescence images and used them to benchmark the performance. Overall, we found comparable and correlated predictive performance for the two imaging modalities. This is promising for future studies of MoAs in time-lapse experiments.

  • 44.
    Gupta, Ankit
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Sabirsh, Alan
    Adv Drug Delivery, Pharmaceut Sci, R&D, S-43150 Mölndal, Sweden..
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Vironova AB, 11330 Gävlegatan 22, Stockholm, Sweden..
    SimSearch: A Human-in-The-Loop Learning Framework for Fast Detection of Regions of Interest in Microscopy Images2022In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 26, no 8, p. 4079-4089Article in journal (Refereed)
    Abstract [en]

    Objective: Large-scale microscopy-based experiments often result in images with rich but sparse information content. An experienced microscopist can visually identify regions of interest (ROIs), but this becomes a cumbersome task with large datasets. Here we present SimSearch, a framework for quick and easy user-guided training of a deep neural model aimed at fast detection of ROIs in large-scale microscopy experiments. Methods: The user manually selects a small number of patches representing different classes of ROIs. This is followed by feature extraction using a pre-trained deep-learning model, and interactive patch selection pruning, resulting in a smaller set of clean (user approved) and larger set of noisy (unapproved) training patches of ROIs and background. The pre-trained deep-learning model is thereafter first trained on the large set of noisy patches, followed by refined training using the clean patches. Results: The framework is evaluated on fluorescence microscopy images from a large-scale drug screening experiment, brightfield images of immunohistochemistry-stained patient tissue samples, and malaria-infected human blood smears, as well as transmission electron microscopy images of cell sections. Compared to state-of-the-art and manual/visual assessment, the results show similar performance with maximal flexibility and minimal a priori information and user interaction. Conclusions: SimSearch quickly adapts to different data sets, which demonstrates the potential to speed up many microscopy-based experiments based on a small amount of user interaction. Significance: SimSearch can help biologists quickly extract informative regions and perform analyses on large datasets helping increase the throughput in a microscopy experiment.

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  • 45.
    Günaydin, Gökce
    et al.
    Karolinska Univ Hosp, Karolinska Inst, Ctr Mol Med, Dept Med Solna,Div Infect Dis, Stockholm, Sweden.
    Edfeldt, Gabriella
    Karolinska Univ Hosp, Karolinska Inst, Ctr Mol Med, Dept Med Solna,Div Infect Dis, Stockholm, Sweden.
    Garber, David A.
    CDC, Natl Ctr HIV AIDS Viral Hepatitis Sexually Transm, Div HIV AIDS Prevent, Lab Branch, Atlanta, GA 30333 USA.
    Asghar, Muhammad
    Karolinska Univ Hosp, Karolinska Inst, Ctr Mol Med, Dept Med Solna,Div Infect Dis, Stockholm, Sweden.
    Noel-Romas, Laura
    Publ Hlth Agcy Canada, JC Wilt Infect Dis Res Ctr, Natl HIV & Retrovirol Labs, Winnipeg, MB, Canada;Univ Manitoba, Dept Obstet & Gynecol, Winnipeg, MB, Canada;Univ Manitoba, Dept Med Microbiol, Winnipeg, MB, Canada.
    Burgener, Adam
    Karolinska Univ Hosp, Karolinska Inst, Ctr Mol Med, Dept Med Solna,Div Infect Dis, Stockholm, Sweden;Publ Hlth Agcy Canada, JC Wilt Infect Dis Res Ctr, Natl HIV & Retrovirol Labs, Winnipeg, MB, Canada;Univ Manitoba, Dept Obstet & Gynecol, Winnipeg, MB, Canada;Univ Manitoba, Dept Med Microbiol, Winnipeg, MB, Canada.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Wang, Lin
    Magee Womens Res Inst, Pittsburgh, PA USA.
    Rohan, Lisa C.
    Magee Womens Res Inst, Pittsburgh, PA USA;Univ Pittsburgh, Sch Pharm, Pittsburgh, PA USA.
    Guenthner, Patricia
    CDC, Natl Ctr HIV AIDS Viral Hepatitis Sexually Transm, Div HIV AIDS Prevent, Lab Branch, Atlanta, GA 30333 USA.
    Mitchell, James
    CDC, Natl Ctr HIV AIDS Viral Hepatitis Sexually Transm, Div HIV AIDS Prevent, Lab Branch, Atlanta, GA 30333 USA.
    Matoba, Nobuyuki
    Univ Louisville, Ctr Predict Med, Louisville, KY 40292 USA;Univ Louisville, Dept Pharmacol & Toxicol, Louisville, KY 40292 USA;Univ Louisville, James Graham Brown Canc Ctr, Louisville, KY 40292 USA.
    McNicholl, Janet M.
    CDC, Natl Ctr HIV AIDS Viral Hepatitis Sexually Transm, Div HIV AIDS Prevent, Lab Branch, Atlanta, GA 30333 USA.
    Palmer, Kenneth E.
    Univ Louisville, Ctr Predict Med, Louisville, KY 40292 USA;Univ Louisville, Dept Pharmacol & Toxicol, Louisville, KY 40292 USA;Univ Louisville, James Graham Brown Canc Ctr, Louisville, KY 40292 USA.
    Tjernlund, Annelie
    Karolinska Univ Hosp, Karolinska Inst, Ctr Mol Med, Dept Med Solna,Div Infect Dis, Stockholm, Sweden.
    Broliden, Kristina
    Karolinska Univ Hosp, Karolinska Inst, Ctr Mol Med, Dept Med Solna,Div Infect Dis, Stockholm, Sweden.
    Impact of Q-Griffithsin anti-HIV microbicide gel in non-human primates: In situ analyses of epithelial and immune cell markers in rectal mucosa2019In: Scientific Reports, E-ISSN 2045-2322, Vol. 9, article id 18120Article in journal (Refereed)
    Abstract [en]

    Natural-product derived lectins can function as potent viral inhibitors with minimal toxicity as shown in vitro and in small animal models. We here assessed the effect of rectal application of an anti-HIV lectin-based microbicide Q-Griffithsin (Q-GRFT) in rectal tissue samples from rhesus macaques. E-cadherin(+) cells, CD4(+) cells and total mucosal cells were assessed using in situ staining combined with a novel customized digital image analysis platform. Variations in cell numbers between baseline, placebo and Q-GRFT treated samples were analyzed using random intercept linear mixed effect models. The frequencies of rectal E-cadherin(+) cells remained stable despite multiple tissue samplings and Q-GRFT gel (0.1%, 0.3% and 1%, respectively) treatment. Whereas single dose application of Q-GRFT did not affect the frequencies of rectal CD4(+) cells, multi-dose Q-GRFT caused a small, but significant increase of the frequencies of intra-epithelial CD4(+) cells (placebo: median 4%; 1% Q-GRFT: median 7%) and of the CD4(+) lamina propria cells (placebo: median 30%; 0.1-1% Q-GRFT: median 36-39%). The resting time between sampling points were further associated with minor changes in the total and CD4(+) rectal mucosal cell levels. The results add to general knowledge of in vivo evaluation of anti-HIV microbicide application concerning cellular effects in rectal mucosa.

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  • 46.
    Hallström, Erik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Kandavalli, Vinodh
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology.
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Sysmex Astrego AB, Uppsala, Sweden..
    Elf, Johan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy2023In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 19, no 11, article id e1011181Article in journal (Refereed)
    Abstract [en]

    Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. For instance, identifying the species and its antibiotic susceptibility is vital for effective bacterial infection treatment. Here we show that phase contrast time-lapse microscopy combined with deep learning is sufficient to classify four species of bacteria relevant to human health. The classification is performed on living bacteria and does not require fixation or staining, meaning that the bacterial species can be determined as the bacteria reproduce in a microfluidic device, enabling parallel determination of susceptibility to antibiotics. We assess the performance of convolutional neural networks and vision transformers, where the best model attained a class-average accuracy exceeding 98%. Our successful proof-of-principle results suggest that the methods should be challenged with data covering more species and clinically relevant isolates for future clinical use. Bacterial infections are a leading cause of premature death worldwide, and growing antibiotic resistance is making treatment increasingly challenging. To effectively treat a patient with a bacterial infection, it is essential to quickly detect and identify the bacterial species and determine its susceptibility to different antibiotics. Prompt and effective treatment is crucial for the patient's survival. A microfluidic device functions as a miniature "lab-on-chip" for manipulating and analyzing tiny amounts of fluids, such as blood or urine samples from patients. Microfluidic chips with chambers and channels have been designed for quickly testing bacterial susceptibility to different antibiotics by analyzing bacterial growth. Identifying bacterial species has previously relied on killing the bacteria and applying species-specific fluorescent probes. The purpose of the herein proposed species identification is to speed up decisions on treatment options by already in the first few imaging frames getting an idea of the bacterial species, without interfering with the ongoing antibiotics susceptibility testing. We introduce deep learning models as a fast and cost-effective method for identifying bacteria species. We envision this method being employed concurrently with antibiotic susceptibility tests in future applications, significantly enhancing bacterial infection treatments.

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  • 47.
    Harrison, Philip J.
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wieslander, Håkan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Sabirsh, Alan
    AstraZeneca R&D.
    Karlsson, Johan
    AstraZeneca R&D.
    Malmsjö, Victor
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hellander, Andreas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Deep-learning models for lipid nanoparticle-based drug delivery2021In: Nanomedicine, ISSN 1743-5889, E-ISSN 1748-6963, Vol. 16, no 13, p. 1097-1110Article in journal (Refereed)
    Abstract [en]

    Background: Early prediction of time-lapse microscopy experiments enables intelligent data management and decision-making. Aim: Using time-lapse data of HepG2 cells exposed to lipid nanoparticles loaded with mRNA for expression of GFP, the authors hypothesized that it is possible to predict in advance whether a cell will express GFP. Methods: The first modeling approach used a convolutional neural network extracting per-cell features at early time points. These features were then combined and explored using either a long short-term memory network (approach 2) or time series feature extraction and gradient boosting machines (approach 3). Results: Accounting for the temporal dynamics significantly improved performance. Conclusion: The results highlight the benefit of accounting for temporal dynamics when studying drug delivery using high-content imaging.

  • 48.
    Harrison, Philip John
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Gupta, Ankit
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Rietdijk, Jonne
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wieslander, Håkan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Carreras-Puigvert, Jordi
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Georgiev, Polina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Evaluating the utility of brightfield image data for mechanism of action prediction2023In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 19, no 7, article id e1011323Article in journal (Refereed)
    Abstract [en]

    Fluorescence staining techniques, such as Cell Painting, together with fluorescence microscopy have proven invaluable for visualizing and quantifying the effects that drugs and other perturbations have on cultured cells. However, fluorescence microscopy is expensive, time-consuming, labor-intensive, and the stains applied can be cytotoxic, interfering with the activity under study. The simplest form of microscopy, brightfield microscopy, lacks these downsides, but the images produced have low contrast and the cellular compartments are difficult to discern. Nevertheless, by harnessing deep learning, these brightfield images may still be sufficient for various predictive purposes. In this study, we compared the predictive performance of models trained on fluorescence images to those trained on brightfield images for predicting the mechanism of action (MoA) of different drugs. We also extracted CellProfiler features from the fluorescence images and used them to benchmark the performance. Overall, we found comparable and largely correlated predictive performance for the two imaging modalities. This is promising for future studies of MoAs in time-lapse experiments for which using fluorescence images is problematic. Explorations based on explainable AI techniques also provided valuable insights regarding compounds that were better predicted by one modality over the other.

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  • 49.
    Holting, Per
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Easy-to-use object selection by color space projections and watershed segmentation2005In: Image Analysis and Processing: ICIAP 2005 13th International Conference, Cagliari, Italy, September 6-8, 2005. Proceedings, 2005, p. 269-276Conference paper (Refereed)
    Abstract [en]

    Digital cameras are gaining in popularity, and not only experts in image analysis, but also the average users, show a growing interest in image processing. Many different kinds of software for image processing offer tools for object selection, or segmentation, but most of them require expertise knowledge, or leave too little freedom in expressing the desired segmentation. This paper presents an easy to use tool for object segmentation in color images. The amount of user interaction is minimized, and no tuning parameters are needed. The method is based on the watershed segmentation algorithm, combined with seeding information given by the user, and color space projections for optimized object edge detection. The presented method can successfully segment objects in most types of color images.

  • 50. Holzwarth, Karolin
    et al.
    Köhler, Ralf
    Philipsen, Lars
    Tokoyoda, Koji
    Ladyhina, Valeriia
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Niesner, Raluca A.
    Hauser, Anja E.
    Multiplexed fluorescence microscopy reveals heterogeneity among stromal cells in mouse bone marrow sections2018In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 93, no 9, p. 876-888Article in journal (Refereed)
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