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  • 1. Acosta, Oscar
    et al.
    Frimmel, Hans
    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, Computerized Image Analysis.
    Fenster, Aaron
    Ourselin, Sébastien
    Filtering and restoration of structures in 3D ultrasound images2007In: Proc. 4th International Symposium on Biomedical Imaging, Piscataway, NJ: IEEE , 2007, 888-891 p.Conference paper (Refereed)
  • 2. Acosta, Oscar
    et al.
    Frimmel, Hans
    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, Computerized Image Analysis.
    Fenster, Aaron
    Salvado, Olivier
    Ourselin, Sébastien
    Pyramidal flux in an anisotropic diffusion scheme for enhancing structures in 3D images2008In: Medical Imaging 2008: Image Processing, Bellingham, WA, 2008, 691429:1-12 p.Conference paper (Refereed)
  • 3.
    Allalou, Amin
    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.
    Methods for 2D and 3D Quantitative Microscopy of Biological Samples2011Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    New microscopy techniques are continuously developed, resulting in more rapid acquisition of large amounts of data. Manual analysis of such data is extremely time-consuming and many features are difficult to quantify without the aid of a computer. But with automated image analysis biologists can extract quantitative measurements and increases throughput significantly, which becomes particularly important in high-throughput screening (HTS). This thesis addresses automation of traditional analysis of cell data as well as automation of both image capture and analysis in zebrafish high-throughput screening. 

    It is common in microscopy images to stain the nuclei in the cells, and to label the DNA and proteins in different ways. Padlock-probing and proximity ligation are highly specific detection methods that  produce point-like signals within the cells. Accurate signal detection and segmentation is often a key step in analysis of these types of images. Cells in a sample will always show some degree of variation in DNA and protein expression and to quantify these variations each cell has to be analyzed individually. This thesis presents development and evaluation of single cell analysis on a range of different types of image data. In addition, we present a novel method for signal detection in three dimensions. 

    HTS systems often use a combination of microscopy and image analysis to analyze cell-based samples. However, many diseases and biological pathways can be better studied in whole animals, particularly those that involve organ systems and multi-cellular interactions. The zebrafish is a widely-used vertebrate model of human organ function and development. Our collaborators have developed a high-throughput platform for cellular-resolution in vivo chemical and genetic screens on zebrafish larvae. This thesis presents improvements to the system, including accurate positioning of the fish which incorporates methods for detecting regions of interest, making the system fully automatic. Furthermore, the thesis describes a novel high-throughput tomography system for screening live zebrafish in both fluorescence and bright field microscopy. This 3D imaging approach combined with automatic quantification of morphological changes enables previously intractable high-throughput screening of vertebrate model organisms.

    List of papers
    1. A detailed analysis of 3D subcellular signal localization
    Open this publication in new window or tab >>A detailed analysis of 3D subcellular signal localization
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    2009 (English)In: Cytometry Part A, ISSN 1552-4922, Vol. 75A, no 4, 319-328 p.Article in journal (Refereed) Published
    Abstract [en]

    Detection and localization of fluorescent signals in relation to other subcellular structures is an important task in various biological studies. Many methods for analysis of fluorescence microscopy image data are limited to 2D. As cells are in fact 3D structures, there is a growing need for robust methods for analysis of 3D data. This article presents an approach for detecting point-like fluorescent signals and analyzing their subnuclear position. Cell nuclei are delineated using marker-controlled (seeded) 3D watershed segmentation. User-defined object and background seeds are given as input, and gradient information defines merging and splitting criteria. Point-like signals are detected using a modified stable wave detector and localized in relation to the nuclear membrane using distance shells. The method was applied to a set of biological data studying the localization of Smad2-Smad4 protein complexes in relation to the nuclear membrane. Smad complexes appear as early as 1 min after stimulation while the highest signal concentration is observed 45 min after stimulation, followed by a concentration decrease. The robust 3D signal detection and concentration measures obtained using the proposed method agree with previous observations while also revealing new information regarding the complex formation.

    Keyword
    3D image analysis, fluorescence signal segmentation, subcellular positioning, Smad detection
    National Category
    Computer and Information Science
    Identifiers
    urn:nbn:se:uu:diva-98014 (URN)10.1002/cyto.a.20663 (DOI)000264513800006 ()
    Available from: 2009-02-05 Created: 2009-02-05 Last updated: 2012-05-09Bibliographically approved
    2. Single-cell A3243G mitochondrial DNA mutation load assays for segregation analysis
    Open this publication in new window or tab >>Single-cell A3243G mitochondrial DNA mutation load assays for segregation analysis
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    2007 (English)In: Journal of Histochemistry and Cytochemistry, ISSN 0022-1554, E-ISSN 1551-5044, Vol. 55, no 11, 1159-1166 p.Article in journal (Refereed) Published
    Abstract [en]

    Segregation of mitochondrial DNA (mtDNA) is an important underlying pathogenic factor in mtDNA mutation accumulation in mitochondrial diseases and aging, but the molecular mechanisms of mtDNA segregation are elusive. Lack of high-throughput single-cell mutation load assays lies at the root of the paucity of studies in which, at the single-cell level, mitotic mtDNA segregation patterns have been analyzed. Here we describe development of a novel fluorescence-based, non-gel PCR restriction fragment length polymorphism method for single-cell A3243G mtDNA mutation load measurement. Results correlated very well with a quantitative in situ Padlock/rolling circle amplification–based genotyping method. In view of the throughput and accuracy of both methods for single-cell A3243G mtDNA mutation load determination, we conclude that they are well suited for segregation analysis.

    Keyword
    A3243G mtDNA, Aging, Heteroplasmy, Mitochondrial diseases, Mutation load, Padlock probing, PCR-RFLP, Segregation
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:uu:diva-12658 (URN)10.1369/jhc.7A7282.2007 (DOI)000250320100009 ()17679731 (PubMedID)
    Available from: 2008-01-09 Created: 2008-01-09 Last updated: 2017-12-11Bibliographically approved
    3. BlobFinder, a tool for fluorescence microscopy image cytometry
    Open this publication in new window or tab >>BlobFinder, a tool for fluorescence microscopy image cytometry
    2009 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 94, no 1, 58-65 p.Article in journal (Refereed) Published
    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.

    Keyword
    Image cytometry, Single cell analysis, FISH, Software
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Research subject
    Computerized Image Analysis
    Identifiers
    urn:nbn:se:uu:diva-87971 (URN)10.1016/j.cmpb.2008.08.006 (DOI)000264282400006 ()18950895 (PubMedID)
    Available from: 2009-01-22 Created: 2009-01-16 Last updated: 2017-12-14Bibliographically approved
    4. Robust signal detection in 3D fluorescence microscopy
    Open this publication in new window or tab >>Robust signal detection in 3D fluorescence microscopy
    2010 (English)In: Cytometry. Part A, ISSN 1552-4922, Vol. 77A, no 1, 86-96 p.Article in journal (Refereed) Published
    Abstract [en]

    Robust detection and localization of biomolecules inside cells is of great importance to better understand the functions related to them. Fluorescence microscopy and specific staining methods make biomolecules appear as point-like signals on image data, often acquired in 3D. Visual detection of such point-like signals can be time consuming and problematic if the 3D images are large, containing many, sometimes overlapping, signals. This sets a demand for robust automated methods for accurate detection of signals in 3D fluorescence microscopy. We propose a new 3D point-source signal detection method that is based on Fourier series. The method consists of two parts, a detector, which is a cosine filter to enhance the point-like signals, and a verifier, which is a sine filter to validate the result from the detector. Compared to conventional methods, our method shows better robustness to noise and good ability to resolve signals that are spatially close. Tests on image data show that the method has equivalent accuracy in signal detection in comparison to Visual detection by experts. The proposed method can be used as an efficient point-like signal detection tool for various types of biological 3D image data.

    National Category
    Bioinformatics and Systems Biology
    Identifiers
    urn:nbn:se:uu:diva-98015 (URN)10.1002/cyto.a.20795 (DOI)000273384700011 ()
    Available from: 2009-02-05 Created: 2009-02-05 Last updated: 2011-11-04Bibliographically approved
    5. High-throughput in vivo optical projection tomography of small vertebrates
    Open this publication in new window or tab >>High-throughput in vivo optical projection tomography of small vertebrates
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    (English)Manuscript (preprint) (Other academic)
    National Category
    Natural Sciences
    Identifiers
    urn:nbn:se:uu:diva-159203 (URN)
    Available from: 2011-09-25 Created: 2011-09-25 Last updated: 2011-11-04
    6. Fully automated cellular-resolution vertebrate screening platform with parallel animal processing
    Open this publication in new window or tab >>Fully automated cellular-resolution vertebrate screening platform with parallel animal processing
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    2012 (English)In: Lab on a Chip, ISSN 1473-0197, E-ISSN 1473-0189, Vol. 12, no 4, 711-716 p.Article in journal (Refereed) Published
    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.

    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:uu:diva-159202 (URN)10.1039/c1lc20849g (DOI)000299380800007 ()
    Available from: 2011-09-25 Created: 2011-09-25 Last updated: 2017-12-08Bibliographically approved
    7. Image based measurements of single cell mtDNA mutation load MTD 2007
    Open this publication in new window or tab >>Image based measurements of single cell mtDNA mutation load MTD 2007
    Show others...
    2007 (English)In: Medicinteknikdagarna 2007, 2007Conference paper, Published 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.

    National Category
    Other Computer and Information Science
    Identifiers
    urn:nbn:se:uu:diva-12745 (URN)
    Available from: 2008-01-11 Created: 2008-01-11 Last updated: 2013-09-25Bibliographically approved
    8. Increasing the dynamic range of in situ PLA
    Open this publication in new window or tab >>Increasing the dynamic range of in situ PLA
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    2011 (English)In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 8, no 11, 892-893 p.Article in journal, Editorial material (Refereed) Published
    National Category
    Biological Sciences
    Identifiers
    urn:nbn:se:uu:diva-159199 (URN)10.1038/nmeth.1743 (DOI)000296891800004 ()
    Available from: 2011-09-25 Created: 2011-09-25 Last updated: 2017-12-08Bibliographically approved
    9. High-throughput cellular-resolution in vivo vertebrate screening
    Open this publication in new window or tab >>High-throughput cellular-resolution in vivo vertebrate screening
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    2011 (English)In: Proc. 15th International Conference on Miniaturized Systems for Chemistry and Life Sciences, 2011Conference paper, Published paper (Refereed)
    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:uu:diva-159201 (URN)
    Available from: 2011-09-25 Created: 2011-09-25 Last updated: 2011-11-04
  • 4.
    Allalou, Amin
    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.
    Curic, Vladimir
    Pardo-Martin, Carlos
    Massachusetts Institute of Technology, USA.
    Yanik, Mehmet Fatih
    Massachusetts Institute of Technology, USA.
    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.
    Approaches for increasing throughput andinformation content of image-based zebrafishscreens2011In: Proceeding of SSBA 2011, 2011Conference paper (Other academic)
    Abstract [en]

    Microscopy in combination with image analysis has emerged as one of the most powerful and informativeways to analyze cell-based high-throughput screening (HTS) samples in experiments designed to uncover novel drugs and drug targets. However, many diseases and biological pathways can be better studied in whole animals, particularly diseases and pathways that involve organ systems and multicellular interactions, such as organ development, neuronal degeneration and regeneration, cancer metastasis, infectious disease progression and pathogenesis. The zebrafish is a wide-spread and popular vertebrate model of human organfunction and development, and it is unique in the sense that large-scale in vivo genetic and chemical studies are feasible due in part to its small size, optical transparency,and aquatic habitat. To improve the throughput and complexity of zebrafish screens, a high-throughput platform for cellular-resolution in vivo chemical and genetic screens on zebrafish larvae has been developed at Yanik lab at Research Laboratory of Electronics, MIT, USA. The system loads live zebrafish from reservoirs or multiwell plates, positions and rotates them for high-speed confocal imaging of organs,and dispenses the animals without damage. We present two improvements to the described system, including automation of positioning of the animals and a novel approach for brightfield microscopy tomographic imaging of living animals.

  • 5.
    Allalou, Amin
    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.
    Wu, Yuelong
    Ghannad-Rezaie, Mostafa
    Eimon, Peter M.
    Yanik, Mehmet Fatih
    Automated deep-phenotyping of the vertebrate brain2017In: eLIFE, E-ISSN 2050-084X, Vol. 6, e23379Article in journal (Refereed)
  • 6. Arvidsson, Anna
    et al.
    Sarve, Hamid
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Johansson, Carina B.
    Comparing and visualizing titanium implant integration in rat bone using 2D and 3D techniques2015In: Journal of Biomedical Materials Research. Part B - Applied biomaterials, ISSN 1552-4973, E-ISSN 1552-4981, Vol. 103, no 1, 12-20 p.Article in journal (Refereed)
    Abstract [en]

    The aim was to compare the osseointegration of grit-blasted implants with and without a hydrogen fluoride treatment in rat tibia and femur, and to visualize bone formation using state-of-the-art 3D visualization techniques. Grit-blasted implants were inserted in femur and tibia of 10 Sprague-Dawley rats (4 implants/rat). Four weeks after insertion, bone implant samples were retrieved. Selected samples were imaged in 3D using Synchrotron Radiation-based CT (SRCT). The 3D data was quantified and visualized using two novel visualization techniques, thread fly-through and 2D unfolding. All samples were processed to cut and ground sections and 2D histomorphometrical comparisons of bone implant contact (BIC), bone area (BA), and mirror image area (MI) were performed. BA values were statistically significantly higher for test implants than controls (p<0.05), but BIC and MI data did not differ significantly. Thus, the results partly indicate improved bone formation at blasted and hydrogen fluoride treated implants, compared to blasted implants. The 3D analysis was a valuable complement to 2D analysis, facilitating improved visualization. However, further studies are required to evaluate aspects of 3D quantitative techniques, with relation to light microscopy that traditionally is used for osseointegration studies. (c) 2014 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 103B: 12-20, 2015.

  • 7.
    Avenel, Christophe
    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.
    Carlbom, Ingrid
    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.
    Blur detection and visualization in histological whole slide images2015In: Proc. 10th International Conference on Mass Data Analysis of Images and Signals, Leipzig, Germany: IBaI , 2015Conference paper (Refereed)
    Abstract [en]

    Digital pathology holds the promise of improved workflow and also of the use of image analysis to extract features from tissue samples for quantitative analysis to improve current subjective analysis of, for example, cancer tissue. But this requires fast and reliable image digitization. In this paper we address image blurriness, which is a particular problem with very large images or tissue micro arrays scanned with whole slide scanners, since autofocus methods may fail when there is a large variation in image content. We introduce a method to detect, quantify and dis-play blurriness from whole slide images (WSI) in real-time. We describe a blurriness measurement based on an ideal high pass filter in the frequency domain. In contrast with other method our method does not require any prior knowledge of the image content, and it produces a continuous blurriness map over the entire WSI. This map can be displayed as an overlay of the original data and viewed at different levels of magnification with zoom and pan features. The computation time for an entire WSI is around 5 minutes on an average workstation, which is about 180 times faster than existing methods.

  • 8.
    Azar, Jimmy
    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.
    Automated Tissue Image Analysis Using Pattern Recognition2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Automated tissue image analysis aims to develop algorithms for a variety of histological applications. This has important implications in the diagnostic grading of cancer such as in breast and prostate tissue, as well as in the quantification of prognostic and predictive biomarkers that may help assess the risk of recurrence and the responsiveness of tumors to endocrine therapy.

    In this thesis, we use pattern recognition and image analysis techniques to solve several problems relating to histopathology and immunohistochemistry applications. In particular, we present a new method for the detection and localization of tissue microarray cores in an automated manner and compare it against conventional approaches.

    We also present an unsupervised method for color decomposition based on modeling the image formation process while taking into account acquisition noise. The method is unsupervised and is able to overcome the limitation of specifying absorption spectra for the stains that require separation. This is done by estimating reference colors through fitting a Gaussian mixture model trained using expectation-maximization.

    Another important factor in histopathology is the choice of stain, though it often goes unnoticed. Stain color combinations determine the extent of overlap between chromaticity clusters in color space, and this intrinsic overlap sets a main limitation on the performance of classification methods, regardless of their nature or complexity. In this thesis, we present a framework for optimizing the selection of histological stains in a manner that is aligned with the final objective of automation, rather than visual analysis.

    Immunohistochemistry can facilitate the quantification of biomarkers such as estrogen, progesterone, and the human epidermal growth factor 2 receptors, in addition to Ki-67 proteins that are associated with cell growth and proliferation. As an application, we propose a method for the identification of paired antibodies based on correlating probability maps of immunostaining patterns across adjacent tissue sections.

    Finally, we present a new feature descriptor for characterizing glandular structure and tissue architecture, which form an important component of Gleason and tubule-based Elston grading. The method is based on defining shape-preserving, neighborhood annuli around lumen regions and gathering quantitative and spatial data concerning the various tissue-types.

    List of papers
    1. Microarray Core Detection by Geometric Restoration
    Open this publication in new window or tab >>Microarray Core Detection by Geometric Restoration
    2012 (English)In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 35, no 5-6, 381-393 p.Article in journal (Refereed) Published
    Abstract [en]

    Whole-slide imaging of tissue microarrays (TMAs) holds the promise of automated image analysis of a large number of histopathological samples from a single slide. This demands high-throughput image processing to enable analysis of these tissue samples for diagnosis of cancer and other conditions. In this paper, we present a completely automated method for the accurate detection and localization of tissue cores that is based on geometric restoration of the core shapes without placing any assumptions on grid geometry. The method relies on hierarchical clustering in conjunction with the Davies-Bouldin index for cluster validation in order to estimate the number of cores in the image wherefrom we estimate the core radius and refine this estimate using morphological granulometry. The final stage of the algorithm reconstructs circular discs from core sections such that these discs cover the entire region of each core regardless of the precise shape of the core. The results show that the proposed method is able to reconstruct core locations without any evidence of localization error. Furthermore, the algorithm is more efficient than existing methods based on the Hough transform for circle detection. The algorithm's simplicity, accuracy, and computational efficiency allow for automated high-throughput analysis of microarray images.

    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:uu:diva-183618 (URN)10.3233/ACP-2012-0067 (DOI)000311675800005 ()22684152 (PubMedID)
    Available from: 2012-10-30 Created: 2012-10-30 Last updated: 2017-12-07Bibliographically approved
    2. Blind Color Decomposition of Histological Images
    Open this publication in new window or tab >>Blind Color Decomposition of Histological Images
    Show others...
    2013 (English)In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 32, no 6, 983-994 p.Article in journal (Refereed) Published
    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.

    National Category
    Medical Image Processing
    Research subject
    Computerized Image Processing
    Identifiers
    urn:nbn:se:uu:diva-160312 (URN)10.1109/TMI.2013.2239655 (DOI)000319701800002 ()
    Available from: 2011-10-21 Created: 2011-10-21 Last updated: 2017-12-08Bibliographically approved
    3. Histological Stain Evaluation for Machine Learning Applications
    Open this publication in new window or tab >>Histological Stain Evaluation for Machine Learning Applications
    2012 (English)In: Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention, 2012Conference paper, Published paper (Refereed)
    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:uu:diva-183619 (URN)
    Conference
    MICCAI 2012, the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, October 1-5, 2012, Nice, France
    Available from: 2012-10-30 Created: 2012-10-30 Last updated: 2015-01-23
    4. Image segmentation and identification of paired antibodies in breast tissue
    Open this publication in new window or tab >>Image segmentation and identification of paired antibodies in breast tissue
    2014 (English)In: Computational & Mathematical Methods in Medicine, ISSN 1748-670X, E-ISSN 1748-6718, 647273:1-11 p.Article in journal (Refereed) Published
    Abstract [en]

    Comparing staining patterns of paired antibodies designed towards a specific protein but toward different epitopes of the protein provides quality control over the binding and the antibodies' ability to identify the target protein correctly and exclusively. We present a method for automated quantification of immunostaining patterns for antibodies in breast tissue using the Human Protein Atlas database. In such tissue, dark brown dye 3,3'-diaminobenzidine is used as an antibody-specific stain whereas the blue dye hematoxylin is used as a counterstain. The proposed method is based on clustering and relative scaling of features following principal component analysis. Our method is able (1) to accurately segment and identify staining patterns and quantify the amount of staining and (2) to detect paired antibodies by correlating the segmentation results among different cases. Moreover, the method is simple, operating in a low-dimensional feature space, and computationally efficient which makes it suitable for high-throughput processing of tissue microarrays.

    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:uu:diva-229978 (URN)10.1155/2014/647273 (DOI)000338856800001 ()25061472 (PubMedID)
    Projects
    eSSENCE
    Available from: 2014-07-01 Created: 2014-08-18 Last updated: 2017-12-05Bibliographically approved
    5. Automated Classification of Glandular Tissue by Statistical Proximity Sampling
    Open this publication in new window or tab >>Automated Classification of Glandular Tissue by Statistical Proximity Sampling
    2015 (English)In: International Journal of Biomedical Imaging, ISSN 1687-4188, E-ISSN 1687-4196, 943104Article in journal (Refereed) Published
    Abstract [en]

    Due to the complexity of biological tissue and variations in staining procedures, features that are based on the explicit extraction of properties from subglandular structures in tissue images may have difficulty generalizing well over an unrestricted set of images and staining variations. We circumvent this problem by an implicit representation that is both robust and highly descriptive, especially when combined with a multiple instance learning approach to image classification. The new feature method is able to describe tissue architecture based on glandular structure. It is based on statistically representing the relative distribution of tissue components around lumen regions, while preserving spatial and quantitative information, as a basis for diagnosing and analyzing different areas within an image. We demonstrate the efficacy of the method in extracting discriminative features for obtaining high classification rates for tubular formation in both healthy and cancerous tissue, which is an important component in Gleason and tubule-based Elston grading. The proposed method may be used for glandular classification, also in other tissue types, in addition to general applicability as a region-based feature descriptor in image analysis where the image represents a bag with a certain label (or grade) and the region-based feature vectors represent instances.

    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:uu:diva-230871 (URN)10.1155/2015/943104 (DOI)000362067400001 ()
    Available from: 2014-09-01 Created: 2014-09-01 Last updated: 2017-12-05Bibliographically approved
  • 9.
    Azar, Jimmy
    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.
    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.
    Histological Stain Evaluation for Machine Learning Applications2012In: Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention, 2012Conference paper (Refereed)
  • 10.
    Azar, Jimmy
    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.
    Busch, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and 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.
    Microarray Core Detection by Geometric Restoration2012In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 35, no 5-6, 381-393 p.Article in journal (Refereed)
    Abstract [en]

    Whole-slide imaging of tissue microarrays (TMAs) holds the promise of automated image analysis of a large number of histopathological samples from a single slide. This demands high-throughput image processing to enable analysis of these tissue samples for diagnosis of cancer and other conditions. In this paper, we present a completely automated method for the accurate detection and localization of tissue cores that is based on geometric restoration of the core shapes without placing any assumptions on grid geometry. The method relies on hierarchical clustering in conjunction with the Davies-Bouldin index for cluster validation in order to estimate the number of cores in the image wherefrom we estimate the core radius and refine this estimate using morphological granulometry. The final stage of the algorithm reconstructs circular discs from core sections such that these discs cover the entire region of each core regardless of the precise shape of the core. The results show that the proposed method is able to reconstruct core locations without any evidence of localization error. Furthermore, the algorithm is more efficient than existing methods based on the Hough transform for circle detection. The algorithm's simplicity, accuracy, and computational efficiency allow for automated high-throughput analysis of microarray images.

  • 11.
    Azar, Jimmy C.
    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.
    Simonsson, Martin
    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.
    Hast, Anders
    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.
    Image segmentation and identification of paired antibodies in breast tissue2014In: Computational & Mathematical Methods in Medicine, ISSN 1748-670X, E-ISSN 1748-6718, 647273:1-11 p.Article in journal (Refereed)
    Abstract [en]

    Comparing staining patterns of paired antibodies designed towards a specific protein but toward different epitopes of the protein provides quality control over the binding and the antibodies' ability to identify the target protein correctly and exclusively. We present a method for automated quantification of immunostaining patterns for antibodies in breast tissue using the Human Protein Atlas database. In such tissue, dark brown dye 3,3'-diaminobenzidine is used as an antibody-specific stain whereas the blue dye hematoxylin is used as a counterstain. The proposed method is based on clustering and relative scaling of features following principal component analysis. Our method is able (1) to accurately segment and identify staining patterns and quantify the amount of staining and (2) to detect paired antibodies by correlating the segmentation results among different cases. Moreover, the method is simple, operating in a low-dimensional feature space, and computationally efficient which makes it suitable for high-throughput processing of tissue microarrays.

  • 12.
    Azar, Jimmy
    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.
    Simonsson, Martin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Hast, Anders
    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 Glandular Tissue by Statistical Proximity Sampling2015In: International Journal of Biomedical Imaging, ISSN 1687-4188, E-ISSN 1687-4196, 943104Article in journal (Refereed)
    Abstract [en]

    Due to the complexity of biological tissue and variations in staining procedures, features that are based on the explicit extraction of properties from subglandular structures in tissue images may have difficulty generalizing well over an unrestricted set of images and staining variations. We circumvent this problem by an implicit representation that is both robust and highly descriptive, especially when combined with a multiple instance learning approach to image classification. The new feature method is able to describe tissue architecture based on glandular structure. It is based on statistically representing the relative distribution of tissue components around lumen regions, while preserving spatial and quantitative information, as a basis for diagnosing and analyzing different areas within an image. We demonstrate the efficacy of the method in extracting discriminative features for obtaining high classification rates for tubular formation in both healthy and cancerous tissue, which is an important component in Gleason and tubule-based Elston grading. The proposed method may be used for glandular classification, also in other tissue types, in addition to general applicability as a region-based feature descriptor in image analysis where the image represents a bag with a certain label (or grade) and the region-based feature vectors represent instances.

  • 13. Bajić, Buda
    et al.
    Lindblad, Joakim
    Sladoje, Nataša
    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.
    An evaluation of potential functions for regularized image deblurring2014In: Image Analysis and Recognition: Part I, Springer Berlin/Heidelberg, 2014, 150-158 p.Conference paper (Refereed)
  • 14.
    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.
    Quantitative and automated microscopy: Where do we stand after 80 years of research?2014In: Proc. 11th International Symposium on Biomedical Imaging, Piscataway, NJ: IEEE Press, 2014, 274-277 p.Conference paper (Refereed)
  • 15.
    Bengtsson, Ewert
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Dahlqvist, Bengt
    Eriksson, Olle
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Jarkrans, Torsten
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Nordin, Bo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Stenkvist, Björn
    Cervical Pre-screening Using Computerized Image Analysis1983In: Proceedings of the 3rd Scandinavian Conference on Image Analysis, Köpenhamn, 1983, 404-411 p.Conference paper (Refereed)
  • 16.
    Bengtsson, Ewert
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Dahlqvist, Bengt
    Eriksson, Olle
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Jarkrans, Torsten
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Nordin, Bo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Stenkvist, Björn
    Studie av reproducerbarheten av mikroskopiska cellbilder med TV-kamera1982Report (Other academic)
  • 17.
    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.
    Malm, Patrik
    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.
    Screening for Cervical Cancer Using Automated Analysis of PAP-Smears2014In: Computational & Mathematical Methods in Medicine, ISSN 1748-670X, E-ISSN 1748-6718, Vol. 2014, 842037:1-12 p.Article, review/survey (Refereed)
    Abstract [en]

    Cervical cancer is one of the most deadly and common forms of cancer among women if no action is taken to prevent it, yet it is preventable through a simple screening test, the so-called PAP-smear. This is the most effective cancer prevention measure developed so far. But the visual examination of the smears is time consuming and expensive and there have been numerous attempts at automating the analysis ever since the test was introduced more than 60 years ago. The first commercial systems for automated analysis of the cell samples appeared around the turn of the millennium but they have had limited impact on the screening costs. In this paper we examine the key issues that need to be addressed when an automated analysis system is developed and discuss how these challenges have been met over the years. The lessons learned may be useful in the efforts to create a cost-effective screening system that could make affordable screening for cervical cancer available for all women globally, thus preventing most of the quarter million annual unnecessary deaths still caused by this disease.

  • 18. Bhatt, Manish
    et al.
    Ayyalasomayajula, Kalyan R.
    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.
    Yalavarthy, Phaneendra K.
    Generalized Beer–Lambert model for near-infrared light propagation in thick biological tissues2016In: Journal of Biomedical Optics, ISSN 1083-3668, E-ISSN 1560-2281, Vol. 21, no 7, 076012Article in journal (Refereed)
  • 19.
    Bianchi, Kevin
    et al.
    ISIT UMR6284 CNRS, Univ. d’Auvergne BP10448, F-63000 Clermont-Ferrand.
    Vacavant, Antoine
    ISIT UMR6284 CNRS, Univ. d’Auvergne BP10448, F-63000 Clermont-Ferrand.
    Strand, Robin
    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.
    Terve, Pierre
    KEOSYS Company 1, impasse Auguste Fresnel, F 44815 Saint Herblain.
    Sarry, Laurent
    ISIT UMR6284 CNRS, Univ. d’Auvergne BP10448, F-63000 Clermont-Ferrand.
    Dual B-spline Snake for Interactive Myocardial Segmentation2013Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel interactive segmentation formalism based on two coupledB-Spline snake models to efficiently and simultaneously extract myocardial walls fromshort-axis magnetic resonance images. The main added value of this model is interactionas it is possible to quickly and intuitively correct the result in complex cases withoutrestarting the whole segmentation working flow. During this process, energies computedfrom the images guide the user to the best position of the model.

  • 20.
    Blom, Elisabeth
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Biochemistry and Organic Chemistry.
    Velikyan, Irina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Biomedical Radiation Sciences.
    Monazzam, Azita
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Endocrine Tumor Biology.
    Razifar, Pasha
    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.
    Nair, Manoj
    Razifar, Payam
    Vanderheyden, Jean-Luc
    Krivoshein, Arcadius V.
    Backer, Marina
    Backer, Joseph
    Långström, Bengt
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Biochemistry and Organic Chemistry.
    Synthesis and characterization of scVEGF-PEG-[68Ga]NOTA and scVEGF-PEG-[68Ga]DOTA PET tracers2011In: Journal of labelled compounds & radiopharmaceuticals, ISSN 0362-4803, E-ISSN 1099-1344, Vol. 54, no 11, 685-692 p.Article in journal (Refereed)
    Abstract [en]

    Vascular endothelial growth factor (VEGF) signaling via vascular endothelial growth factor receptor 2 (VEGFR-2) on tumor endothelial cells is a critical driver of tumor angiogenesis. Novel anti-angiogenic drugs target VEGF/VEGFR-2 signaling and induce changes in VEGFR-2 prevalence. To monitor VEGFR-2 prevalence in the course of treatment, we are evaluating (68)Ga positron emission tomography imaging agents based on macrocyclic chelators, site-specifically conjugated via polyethylene glycol (PEG) linkers to engineered VEGFR-2 ligand, single-chain (sc) VEGF. The (68)Ga-labeling was performed at room temperature with NOTA (2,2', 2 ''-(1,4,7-triazonane-1,4,7-triyl) triacetic acid) conjugates or at 90 degrees C by using either conventional or microwave heating with NOTA and DOTA (2,2', 2 '', 2'''-(1,4,7,10-tetraazacyclododecane-1,4,7,10-tetrayl) tetraacetic acid) conjugates. The fastest (similar to 2min) and the highest incorporation (>90%) of (68)Ga into conjugate that resulted in the highest specific radioactivity (similar to 400MBq/nmol) was obtained with microwave heating of the conjugates. The bioactivity of the NOTA-and DOTA-containing tracers was validated in 3-D tissue culture model of 293/KDR cells engineered to express high levels of VEGFR-2. The NOTA-containing tracer also displayed a rapid accumulation (similar to 20s after intravenous injection) to steady-state level in xenograft tumor models. A combination of high specific radioactivity and maintenance of functional activity suggests that scVEGF-PEG-[(68)Ga] NOTA and scVEGF-PEG-[(68)Ga] DOTA might be promising tracers for monitoring VEGFR-2 prevalence and should be further explored.

  • 21.
    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, 1068-1077 p.Article in journal (Refereed)
  • 22.
    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
    Nilsson, Mats
    Wählby, Carolina
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Decoding gene expression in 2D and 3D2017In: Image Analysis: Part II, Springer, 2017, 257-268 p.Conference paper (Refereed)
  • 23.
    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.

  • 24.
    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.

  • 25.
    Carlbom, Ingrid
    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.
    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.
    Busch, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Picro-Sirius-HTX Stain for Blind Color Decomposition of Histopathological Prostate Tissue2014In: Proc, IEEE 11th International Symposium on Biomedical Imaging (ISBI) 2014, 2014, 282-285 p.Conference paper (Refereed)
    Abstract [en]

    Gleason grading is the most widely used system for determining the severity of prostate cancer. The Gleason grade is determined visually under a microscope from prostate tissue that is most often stained with Hematoxylin-Eosin (H&E). In an earlier study we demonstrated that this stain is not ideal for machine learning applications, but that other stains, such as Sirius-hematoxylin (Sir-Htx), may perform better. In this paper we illustrate the advantages of this stain over H&E for blind color decomposition. When compared to ground truth defined by an experienced pathologist, the relative root-mean-square errors of the color decomposition mixing matrices for Sir-Htx are better than those for H&E by a factor of two, and the Pearson correlation coefficients of the density maps resulting from the decomposition of Sir-Htx-stained tissue gives a 99% correlation with the ground truth. Qualitative examples of the density maps confirm the quantitative findings and illustrate that the density maps will allow accurate segmentation of morphological features that determine the Gleason grade.

  • 26. Chandran, P. S.
    et al.
    Byju, N. B.
    Deepak, R. U.
    Rajesh Kumar, R.
    Sudhamony, S.
    Malm, Patrik
    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.
    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.
    Cluster detection in cytology images using the cellgraph method2012In: Information Technology in Medicine and Education (ITME), 2012 International Symposium, 2012, 923-927 p.Conference paper (Refereed)
    Abstract [en]

    Automated cervical cancer detection system is primarily based on delineating the cell nuclei and analyzing their textural and morphometric features for malignant characteristics. The presence of cell clusters in the slides have diagnostic value, since malignant cells have a greater tendency to stick together forming clusters than normal cells. However, cell clusters pose difficulty in delineating nucleus and extracting features reliably for malignancy detection in comparison to free lying cells. LBC slide preparation techniques remove biological artifacts and clustering to some extent but not completely. Hence cluster detection in automated cervical cancer screening becomes significant. In this work, a graph theoretical technique is adopted which can identify and compute quantitative metrics for this purpose. This method constructs a cell graph of the image in accordance with the Waxman model, using the positional coordinates of cells. The computed graph metrics from the cell graphs are used as the feature set for the classifier to deal with cell clusters. It is a preliminary exploration of using the topological analysis of the cellgraph to cytological images and the accuracy of classification using SVM showed that the results are well suited for cluster detection.

  • 27.
    Chantzi, Efthymia
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Biology Education Centre.
    Fast processing of label-free video microscopy movies of human and bacterial cell populations growing in vitro during chemical exposure2016Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    A fast computational framework for large-scale parallel processing of label-free video microscopy movies of human and bacterial cell populations growing in vitro during chemical exposure was developed in MATLAB®. The overarching aim was to quantify and study time evolving morphological effects due to chemical perturbations caused by single drugs and combinations. Using this framework, a previously reported method for characterization of differences in time evolving morphologies of human cell populations, based on pixel histogram hierarchies of phase-contrast microscopy images, was re-implemented, refined and subsequently optimized with respect to method-specific tuning parameters. This implementation  was also generalized for time-lapse microscopy movies of bacterial cell cultures, generated by the oCelloScope™ system, which acquires multiple series of images of non-adherent cell populations in the cell culture medium. In addition, a separate computational framework for large-scale parallel quantification of the bacterial growth was deployed as an alternative to the growth kinetics analysis provided by the integrated commercial software of the oCelloScope™ system. The potential of the implemented frameworks was demonstrated on experimental data by processing time-lapse movies from different human and bacterial cell populations, while being exposed to different single chemical compounds and combinations. These novel computational tools are compatible with either single high-end multi-core computers or cloud-based distributed computing infrastructures offered via MapReduce, and Hadoop® MapReduce, respectively. This enables fast and fault-tolerant processing of huge video microscopy datasets and opens for optimization of user-defined tuning parameters.

  • 28.
    Choi, Heung-Kook
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    New Methods for Image Analysis of Tissue Sections1996Doctoral thesis, comprehensive summary (Other academic)
  • 29.
    Christersson, Albert
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Orthopaedics.
    Nysjö, Johan
    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.
    Berglund, Lars
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, UCR-Uppsala Clinical Research Center.
    Malmberg, Filip
    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.
    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.
    Nyström, Ingela
    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.
    Larsson, Sune
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Orthopaedics.
    Comparison of 2D radiography and a semi-automatic CT-based 3D method for measuring change in dorsal angulation over time in distal radius fractures2016In: Skeletal Radiology, ISSN 0364-2348, E-ISSN 1432-2161, Vol. 45, no 6, 763-769 p.Article in journal (Refereed)
    Abstract [en]

    Objective The aim of the present study was to compare the reliability and agreement between a computer tomography-based method (CT) and digitalised 2D radiographs (XR) when measuring change in dorsal angulation over time in distal radius fractures. Materials and methods Radiographs from 33 distal radius fractures treated with external fixation were retrospectively analysed. All fractures had been examined using both XR and CT at six times over 6 months postoperatively. The changes in dorsal angulation between the first reference images and the following examinations in every patient were calculated from 133 follow-up measurements by two assessors and repeated at two different time points. The measurements were analysed using Bland-Altman plots, comparing intra- and inter-observer agreement within and between XR and CT. Results The mean differences in intra- and inter-observer measurements for XR, CT, and between XR and CT were close to zero, implying equal validity. The average intra- and inter-observer limits of agreement for XR, CT, and between XR and CT were +/- 4.4 degrees, +/- 1.9 degrees and +/- 6.8 degrees respectively. Conclusions For scientific purpose, the reliability of XR seems unacceptably low when measuring changes in dorsal angulation in distal radius fractures, whereas the reliability for the semi-automatic CT-based method was higher and is therefore preferable when a more precise method is requested.

  • 30. Ciesielski, Krzysztof Chris
    et al.
    Strand, Robin
    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 Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
    Malmberg, Filip
    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 Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
    Saha, Punam K.
    Efficient algorithm for finding the exact minimum barrier distance2014In: Computer Vision and Image Understanding, ISSN 1077-3142, E-ISSN 1090-235X, Vol. 123, 53-64 p.Article in journal (Refereed)
  • 31. Clausson, Carl-Magnus
    et al.
    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, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 5, 12317:1-10 p., 12317Article in journal (Refereed)
  • 32.
    Dahlqvist, Bengt
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Scientific Computing.
    Application of Decision Models to Some Problems in Image Analysis1988Doctoral thesis, comprehensive summary (Other academic)
  • 33.
    Dahlqvist, Bengt
    et al.
    Uppsala University.
    Nordin, Bo
    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.
    Recognition and Classification of Cancer Cells in an Image Analysis System1988Report (Other academic)
  • 34. Deepak, Rajasekharan Usha
    et al.
    Kumar, Ramakrishnan Rajesh
    Byju, Neendoorthalackal Balakrishnan
    Sharathkumar, Pundluvalu Nataraju
    Pournami, Chandran
    Sibi, Salam
    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.
    Sujathan, Kunjuraman
    Computer Assisted Pap Smear Analyser for Cervical Cancer Screening using Quantitative Microscopy2015In: Journal of Cytology & Histology, ISSN 2157-7099, Vol. 6, no S3, 010Article in journal (Refereed)
  • 35. den Hollander, Lianne
    et al.
    Han, HongMei
    de Winter, Matthijs
    Svensson, Lennart
    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.
    Masich, Sergej
    Daneholt, Bertil
    Norlén, Lars
    Skin lamellar bodies are not discrete vesicles but part of a tubuloreticular network2016In: Acta Dermato-Venereologica, ISSN 0001-5555, E-ISSN 1651-2057, Vol. 96, no 3, 303-309 p.Article in journal (Refereed)
  • 36. Dong, Pei
    et al.
    Pacureanu, Alexandra
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Zuluaga, Maria A.
    Olivier, Cecile
    Grimal, Quentin
    Peyrin, Francoise
    Quantification of the 3D Morphology of the Bone Cell Network From Synchrotron Micro-Ct Images2014In: Image Analysis and Stereology, ISSN 1580-3139, E-ISSN 1854-5165, Vol. 33, no 2, 157-166 p.Article in journal (Refereed)
    Abstract [en]

    In the context of bone diseases research, recent works have highlighted the crucial role of the osteocyte system. This system, hosted in the lacuno-canalicular network (LCN), plays a key role in the bone remodeling process. However, few data are available on the LCN due to the limitations of current microscopy techniques, and have mainly only been obtained from 2D histology sections. Here we present, for the first time, an automatic method to quantify the LCN in 3D from synchrotron radiation micro-tomography images. After segmentation of the LCN, two binary images are generated, one of lacunae (hosting the cell body) and one of canaliculi (small channels linking the lacunae). The binary image of lacunae is labeled, and for each object, lacunar descriptors are extracted after calculating the second order moments and the intrinsic volumes. Furthermore, we propose a specific method to quantify the ramification of canaliculi around each lacuna. To this aim, a signature of the numbers of canaliculi at different distances from the lacunar surface is estimated through the calculation of topological parameters. The proposed method was applied to the 3D SR micro-CT image of a human femoral mid-diaphysis bone sample. Statistical results are reported on 399 lacunae and their surrounding canaliculi.

  • 37.
    Dražić, Slobodan
    et al.
    Faculty of Technical Sciences, University of Novi Sad, Serbia.
    Lindblad, Joakim
    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.
    Sladoje, Natasa
    Faculty of Technical Sciences, University of Novi Sad, Serbia.
    Precise Estimation of the Projection of a Shape from a Pixel Coverage Representation2011In: Proceedings of the 7th IEEE International Symposium on Image and Signal Processing and Analysis (ISPA), IEEE Computer Society, 2011, 569-574 p.Conference paper (Refereed)
    Abstract [en]

    Measuring width and diameter of a shape areproblems well studied in the literature. A pixel coverage repre-sentation is one specific type of digital fuzzy representation of acontinuous image object, where the (membership) value of eachpixel is (approximately) equal to the relative area of the pixelwhich is covered by the continuous object. Lately a number ofmethods for shape analysis use pixel coverage for reducing errorof estimation. We introduce a novel method for estimating theprojection of a shape in a given direction. The method is based onutilizing pixel coverage representation of a shape. Performance ofthe method is evaluated by a number of tests on synthetic objects,confirming high precision and applicability for calculation ofdiameter and elongation of a shape.

  • 38.
    Egevad, Lars
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Frimmel, Hans
    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.
    Mattson, Stefan
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Information Science, Statistics.
    Bengtsson, Ewert
    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.
    Busch, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Biopsy protocol stability in a three-dimensional model of prostate cancer: Changes in cancer yield after adjustment of biopsy positions1999In: Urology, ISSN 0090-4295, E-ISSN 1527-9995, Vol. 54, 862-868 p.Article in journal (Refereed)
  • 39.
    Egevad, Lars
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Frimmel, Hans
    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.
    Norberg, Mona
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Oncology, Radiology and Clinical Immunology, Radiology.
    Mattson, Stefan
    Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Information Science, Statistics.
    Carlbom, Ingrid
    Bengtsson, Ewert
    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.
    Busch, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Three-dimensional computer reconstruction of prostate cancer from radical prostatectomy specimens: Evaluation of the model by core biopsy simulation1999In: Urology, ISSN 0090-4295, E-ISSN 1527-9995, Vol. 53, 192-198 p.Article in journal (Refereed)
  • 40.
    Erkers, Julia
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Biology Education Centre.
    Towards automatic smartphone analysis for point-of-care microarray assays2016Independent thesis Advanced level (professional degree), 300 HE creditsStudent thesis
    Abstract [en]

    Poverty and long distances are two reasons why some people in the third world countries hasdifficulties seeking medical help. A solution to the long distances could be if the medical carewas more mobile and diagnostically tests could be performed on site in villages. A new pointof-care test based on a small blood shows promising results both in run time and mobility.However, the method still needs more advanced equipment for analysis of the resultingmicroarray. This study has investigated the potential to perform the analysis within asmartphone application, performing all steps from image capturing to a diagnostic result. Theproject was approach in two steps, starting with implementation and selection of imageanalysis methods and finishing with implementing those results into an Android application.A final application was not developed, but the results gained from this project indicates that asmartphone processing power is enough to perform heavy image analysis within a sufficientamount of time. It also imply that the resolution in the evaluated images taken with a Nexus 6together with an external macro lens most likely is enough for the whole analysis, but furtherwork must be done to ensure it.

  • 41. Etterlin, P. E.
    et al.
    Ekman, S.
    Strand, Robin
    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.
    Olstad, K.
    Ley, C. J.
    Osteochondrosis, Synovial Fossae, and Articular Indentations in the Talus and Distal Tibia of Growing Domestic Pigs and Wild Boars2017In: Veterinary pathology, ISSN 0300-9858, E-ISSN 1544-2217, Vol. 54, no 3, 445-456 p.Article in journal (Refereed)
  • 42.
    Fakhrzadeh, Azadeh
    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.
    Computerized Cell and Tissue Analysis2015Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The latest advances in digital cameras combined with powerful computer software enable us to store high-quality microscopy images of specimen. Studying hundreds of images manually is very time consuming and has the problem of human subjectivity and inconsistency. Quantitative image analysis is an emerging field and has found its way into analysis of microscopy images for clinical and research purposes. When developing a pipeline, it is important that its components are simple enough to be generalized and have predictive value. This thesis addresses the automation of quantitative analysis of tissue in two different fields: pathology and plant biology.

    Testicular tissue is a complex structure consisting of seminiferous tubules. The epithelial layer of a seminiferous tubule contains cells that differentiate from primitive germ cells to spermatozoa in a number of steps. These steps are combined in 12 stages in the cycle of the seminiferous epithelium in the mink. The society of toxicological pathology recommends classifying the testicular epithelial into different stages when assessing tissue damage to determine if the dynamics in the spermatogenic cycle have been disturbed. This thesis presents two automated methods for fast and robust segmentation of tubules, and an automated method of staging them. For better accuracy and statistical analysis, we proposed to pool stages into 5 groups. This pooling is suggested based on the morphology of tubules. In the 5 stage case, the overall number of correctly classified tubules is 79.6%.

    Contextual information on the localization of fluorescence in microscopy images of plant specimen help us to better understand differentiation and maturation of stem cells into tissues. We propose a pipeline for automated segmentation and classification of the cells in a whole cross-section of Arabidopsis hypocotyl, stem, or root. As proof-of-concept that the classification provides a meaningful basis to group cells for fluorescence characterization, we probed tissues with an antibody specific to xylem vessels in the secondary cell wall. Fluorescence intensity in different classes of cells is measured by the pipeline. The measurement results clearly show that the xylem vessels are the dominant cell type that exhibit a fluorescence signal.

    List of papers
    1. Analyzing Tubular Tissue in Histopathological Thin Sections
    Open this publication in new window or tab >>Analyzing Tubular Tissue in Histopathological Thin Sections
    2012 (English)In: 2012 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING TECHNIQUES AND APPLICATIONS (DICTA), IEEE conference proceedings, 2012, 1-6 p.Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    IEEE conference proceedings, 2012
    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:uu:diva-188548 (URN)10.1109/DICTA.2012.6411735 (DOI)000316318400071 ()
    Conference
    International Conference on Digital Image Computing Techniques and Applications (DICTA), 3-5 Dec, 2012, Fremantle, AUSTRALIA
    Available from: 2012-12-17 Created: 2012-12-17 Last updated: 2015-06-03Bibliographically approved
    2. Epithelial Cell Segmentation in Histological Images of Testicular Tissue Using Graph-Cut
    Open this publication in new window or tab >>Epithelial Cell Segmentation in Histological Images of Testicular Tissue Using Graph-Cut
    2013 (English)In: Image Analysis and Processing – ICIAP 2013: Part II, 2013, 201-208 p.Conference paper, Published paper (Refereed)
    Abstract [en]

    Computerized image processing has provided us with valuable tools for analyzing histology images. However, histology images are complex, and the algorithm which is developed for a data set may not work for a new and unseen data set. The preparation procedure of the tissue before imaging can significantly affect the resulting image. Even for the same staining method, factors like delayed fixation may alter the image quality. In this paper we face the challenging problem of designing a method that works on data sets with strongly varying quality. In environmental research, due to the distance between the site where the wild animals are caught and the laboratory, there is always a delay in fixation. Here we suggest a segmentation method based on the structural information of epithelium cell layer in testicular tissue. The cell nuclei are detected using the fast radial symmetry filter. A graph is constructed on top of the epithelial cells. Graph-cut optimization method is used to cut the links between cells of different tubules. The algorithm is tested on five different groups of animals. Group one is fixed immediately, three groups were left at room temperature for 18, 30 and 42 hours respectively, before fixation. Group five was frozen after 6 hours in room temperature and thawed. The suggested algorithm gives promising results for the whole data set.

    Series
    Lecture Notes in Computer Science, ISSN 0302-9743 ; 8157
    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:uu:diva-210299 (URN)10.1007/978-3-642-41184-7_21 (DOI)000329811200021 ()978-3-642-41183-0 (ISBN)978-3-642-41184-7 (ISBN)
    Conference
    17th International Conference on Image Analysis and Processing (ICIAP), Naples, Italy, September 9-13, 2013
    Available from: 2013-11-05 Created: 2013-11-05 Last updated: 2015-06-03Bibliographically approved
    3. Computerized Study of Developmental Stages in Mink Testicular Tissue
    Open this publication in new window or tab >>Computerized Study of Developmental Stages in Mink Testicular Tissue
    Show others...
    (English)Manuscript (preprint) (Other academic)
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:uu:diva-252411 (URN)
    Available from: 2015-05-06 Created: 2015-05-06 Last updated: 2015-06-03
    4. Precision automation of cell type classification and sub-cellular fluorescence quantification from laser scanning confocal images
    Open this publication in new window or tab >>Precision automation of cell type classification and sub-cellular fluorescence quantification from laser scanning confocal images
    2016 (English)In: Frontiers in Plant Science, ISSN 1664-462X, E-ISSN 1664-462X, Vol. 7, 119Article in journal (Refereed) Published
    Abstract [en]

    While novel whole-plant phenotyping technologies have been successfully implemented into functional genomics and breeding programs, the potential of automated phenotyping with cellular resolution is largely unexploited. Laser scanning confocal microscopy has the potential to close this gap by providing spatially highly resolved images containing anatomic as well as chemical information on a subcellular basis. However, in the absence of automated methods, the assessment of the spatial patterns and abundance of fluorescent markers with subcellular resolution is still largely qualitative and time-consuming. Recent advances in image acquisition and analysis, coupled with improvements in microprocessor performance, have brought such automated methods within reach, so that information from thousands of cells per image for hundreds of images may be derived in an experimentally convenient time-frame. Here, we present a MATLAB-based analytical pipeline to (1) segment radial plant organs into individual cells, (2) classify cells into cell type categories based upon Random Forest classification, (3) divide each cell into sub-regions, and (4) quantify fluorescence intensity to a subcellular degree of precision for a separate fluorescence channel. In this research advance, we demonstrate the precision of this analytical process for the relatively complex tissues of Arabidopsis hypocotyls at various stages of development. High speed and robustness make our approach suitable for phenotyping of large collections of stem-like material and other tissue types.

    Keyword
    automated image analysis; confocal microscopy; Arabidopsis; hypocotyl; automated phenotyping; code:matlab
    National Category
    Plant Biotechnology
    Research subject
    Computerized Image Processing
    Identifiers
    urn:nbn:se:uu:diva-252412 (URN)10.3389/fpls.2016.00119 (DOI)000369802700001 ()
    Funder
    Bio4EnergyVINNOVA
    Available from: 2016-02-09 Created: 2015-05-06 Last updated: 2017-12-04Bibliographically approved
    5. Effect of pre-fixation delay and freezing on mink testicular endpoints for environmental research
    Open this publication in new window or tab >>Effect of pre-fixation delay and freezing on mink testicular endpoints for environmental research
    Show others...
    2015 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 5, e0125139Article in journal (Refereed) Published
    National Category
    Veterinary Science
    Research subject
    Computerized Image Processing
    Identifiers
    urn:nbn:se:uu:diva-252410 (URN)10.1371/journal.pone.0125139 (DOI)000353887100081 ()25933113 (PubMedID)
    Available from: 2015-05-01 Created: 2015-05-06 Last updated: 2017-12-04Bibliographically approved
  • 43.
    Fakhrzadeh, Azadeh
    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.
    Sporndly-Nees, Ellinor
    Swedish Univ Agr Sci, Dept Anat Physiol & Biochem, Uppsala, Sweden..
    Ekstedt, Elisabeth
    Swedish Univ Agr Sci, Dept Anat Physiol & Biochem, Uppsala, Sweden..
    Holm, Lena
    Swedish Univ Agr Sci, Dept Anat Physiol & Biochem, Uppsala, Sweden..
    Luengo Hendriks, Cris L.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. U.
    New computerized staging method to analyze mink testicular tissue in environmental research2017In: Environmental Toxicology and Chemistry, ISSN 0730-7268, E-ISSN 1552-8618, Vol. 36, no 1, 156-164 p.Article in journal (Refereed)
    Abstract [en]

    Histopathology of testicular tissue is considered to be the most sensitive tool to detect adverse effects on male reproduction. When assessing tissue damage, seminiferous epithelium needs to be classified into different stages to detect certain cell damages; but stage identification is a demanding task. The authors present a method to identify the 12 stages in mink testicular tissue. The staging system uses Gata-4 immunohistochemistry to visualize acrosome development and proved to be both intraobserver-reproducible and interobserver-reproducible with a substantial agreement of 83.6% (kappa=0.81) and 70.5% (kappa=0.67), respectively. To further advance and objectify this method, they present a computerized staging system that identifies these 12 stages. This program has an agreement of 52.8% (kappa 0.47) with the consensus staging by 2 investigators. The authors propose a pooling of the stages into 5 groups based on morphology, stage transition, and toxicologically important endpoints. The computerized program then reached a substantial agreement of 76.7% (kappa=0.69). The computerized staging tool uses local ternary patterns to describe the texture of the tubules and a support vector machine classifier to learn which textures correspond to which stages. The results have the potential to modernize the tedious staging process required in toxicological evaluation of testicular tissue, especially if combined with whole-slide imaging and automated tubular segmentation. Environ Toxicol Chem 2017;36:156-164.

  • 44.
    Fakhrzadeh, Azadeh
    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.
    Spörndly-Nees, Ellinor
    Swedish University of Agricultural Sciences.
    Holm, Lena
    Swedish University of Agricultural Sciences.
    Luengo Hendriks, Cris L.
    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.
    Analyzing Tubular Tissue in Histopathological Thin Sections2012In: 2012 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING TECHNIQUES AND APPLICATIONS (DICTA), IEEE conference proceedings, 2012, 1-6 p.Conference paper (Refereed)
  • 45.
    Fakhrzadeh, Azadeh
    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.
    Spörndly-Nees, Ellinor
    Swedish University of Agricultural Sciences.
    Holm, Lena
    Swedish University of Agricultural Sciences.
    Luengo Hendriks, Cris L.
    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.
    Automated measurement of epithelial height of testicular tissue2012In: Proceedings of Swedish Society for Image Analysis, SSBA 2012, Stockholm: KTH Royal Institute of Technology, 2012Conference paper (Other academic)
  • 46.
    Fakhrzadeh, Azadeh
    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.
    Spörndly-Nees, Ellinor
    Swedish University of Agricultural Sciences.
    Holm, Lena
    Swedish University of Agricultural Sciences.
    Luengo Hendriks, Cris L.
    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.
    Epithelial Cell Layer Segmentation UsingGraph-cut and Its Application in TesticularTissue2013Conference paper (Refereed)
  • 47.
    Fakhrzadeh, Azadeh
    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.
    Spörndly-Nees, Ellinor
    Swedish University of Agricultural Sciences.
    Holm, Lena
    Swedish University of Agricultural Sciences.
    Luengo Hendriks, Cris L.
    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.
    Epithelial Cell Segmentation in Histological Images of Testicular Tissue Using Graph-Cut2013In: Image Analysis and Processing – ICIAP 2013: Part II, 2013, 201-208 p.Conference paper (Refereed)
    Abstract [en]

    Computerized image processing has provided us with valuable tools for analyzing histology images. However, histology images are complex, and the algorithm which is developed for a data set may not work for a new and unseen data set. The preparation procedure of the tissue before imaging can significantly affect the resulting image. Even for the same staining method, factors like delayed fixation may alter the image quality. In this paper we face the challenging problem of designing a method that works on data sets with strongly varying quality. In environmental research, due to the distance between the site where the wild animals are caught and the laboratory, there is always a delay in fixation. Here we suggest a segmentation method based on the structural information of epithelium cell layer in testicular tissue. The cell nuclei are detected using the fast radial symmetry filter. A graph is constructed on top of the epithelial cells. Graph-cut optimization method is used to cut the links between cells of different tubules. The algorithm is tested on five different groups of animals. Group one is fixed immediately, three groups were left at room temperature for 18, 30 and 42 hours respectively, before fixation. Group five was frozen after 6 hours in room temperature and thawed. The suggested algorithm gives promising results for the whole data set.

  • 48. Freitag, C.
    et al.
    Noble, C.
    Fritzsche, J.
    Persson, F
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
    Reiter-Schad, M.
    Nilsson, A. N.
    Graneli, A.
    Ambjoernsson, T.
    Mir, K. U.
    Tegenfeldt, J. O.
    Visualizing the entire DNA from a chromosome in a single frame2015In: Biomicrofluidics, ISSN 1932-1058, E-ISSN 1932-1058, Vol. 9, no 4, 044114Article in journal (Refereed)
    Abstract [en]

    The contiguity and phase of sequence information are intrinsic to obtain complete understanding of the genome and its relationship to phenotype. We report the fabrication and application of a novel nanochannel design that folds megabase lengths of genomic DNA into a systematic back-and-forth meandering path. Such meandering nanochannels enabled us to visualize the complete 5.7 Mbp (1mm) stained DNA length of a Schizosaccharomyces pombe chromosome in a single frame of a CCD. We were able to hold the DNA in situ while implementing partial denaturation to obtain a barcode pattern that we could match to a reference map using the Poland-Scheraga model for DNA melting. The facility to compose such long linear lengths of genomic DNA in one field of view enabled us to directly visualize a repeat motif, count the repeat unit number, and chart its location in the genome by reference to unique barcode motifs found at measurable distances from the repeat. Meandering nanochannel dimensions can easily be tailored to human chromosome scales, which would enable the whole genome to be visualized in seconds.

  • 49.
    Frimmel, Hans
    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.
    Biopsy Needle Optimisation1997In: Proc. 10th Scandinavian Conference on Image Analysis, Pattern Recognition Society of Finland , 1997, 381-387 p.Conference paper (Refereed)
  • 50.
    Frimmel, Hans
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Positioning Biopsy Needles in the Prostate Gland Using 3D Computer Modelling1999Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In the world of medicine, image diagnostics have, until recently, been based merely on two dimensional information sources. The understanding of three dimensional structures has been limited to creating mental images in the mind of the physician, to wax models and to autopsy. In the last few years, computers have made it possible to model and reconstruct real three dimensional objects and thus give the physician a new tool not only to describe localisation and distribution patterns of diseases, above all cancer, but also as an aid in the understanding of the human body. This thesis contributes in the development of such tools, based on a specific application.

    Prostate cancer is for men the most common form of cancer. Improvement in diagnostics for this form of cancer would facilitate planning of treatment and hence save, and preserve the quality of, life. One way to diagnose and quantify prostate cancer is to assess its presence and malignancy grade in cylindrical tissue samples taken with a needle biopsy device. Today, two to six such samples are generally taken, with poorly standardised rules for the positioning of the needle, thus interindividual variation exists.

    In this thesis, 3D models to analyse the problem with the positioning of biopsy needles have been developed. By using information from physical prostates removed from patients by surgery, a 3D cancer probability distribution has been built. Using this information, a standardised biopsy needle protocol has been created that is efficient, stable and easy to use. In this process new methods for morphing images, registrating slices and optimising positions for use with computer modelling have been developed.

    Many physicians were involved in the study. Thus, an important part of the work has been to make every part of the work understandable for people without special computer programming knowledge. Also, efforts have been made to make it possible to easily examine every piece of information created in order to verify the correctness of the methods used.

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