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

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

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

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

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

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

  • 3.
    Allalou, Amin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    van de Rijke, Frans
    Jahangir Tafrechi, Roos
    Raap, Anton
    Segmentation of Cytoplasms of Cultured Cells2007In: In Proceedings SSBA 2007, Symposium on image analysis, Linköping, 2007Conference paper (Other academic)
    Abstract [en]

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

  • 4.
    Bengtsson, Ewert
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Robust cell image segmentation methods.2004In: Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, ISSN 1054-6618, Vol. 14, no 2, 157-167 p.Article in journal (Refereed)
    Abstract [en]

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

  • 5.
    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)
  • 6.
    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)
  • 7.
    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.

  • 8.
    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.

  • 9. Carreras-Puigvert, Jordi
    et al.
    Zitnik, Marinka
    Jemth, Ann-Sofie
    Carter, Megan
    Unterlass, Judith E
    Hallström, Björn
    Loseva, Olga
    Karem, Zhir
    Calderón-Montaño, José Manuel
    Lindskog, Cecilia
    Edqvist, Per-Henrik
    Matuszewski, Damian J
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Ait Blal, Hammou
    Berntsson, Ronnie P A
    Häggblad, Maria
    Martens, Ulf
    Studham, Matthew
    Lundgren, Bo
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sonnhammer, Erik L L
    Lundberg, Emma
    Stenmark, Pål
    Zupan, Blaz
    Helleday, Thomas
    A comprehensive structural, biochemical and biological profiling of the human NUDIX hydrolase family.2017In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Nature communications, ISSN 2041-1723, Vol. 8, no 1, 1541Article in journal (Refereed)
    Abstract [en]

    The NUDIX enzymes are involved in cellular metabolism and homeostasis, as well as mRNA processing. Although highly conserved throughout all organisms, their biological roles and biochemical redundancies remain largely unclear. To address this, we globally resolve their individual properties and inter-relationships. We purify 18 of the human NUDIX proteins and screen 52 substrates, providing a substrate redundancy map. Using crystal structures, we generate sequence alignment analyses revealing four major structural classes. To a certain extent, their substrate preference redundancies correlate with structural classes, thus linking structure and activity relationships. To elucidate interdependence among the NUDIX hydrolases, we pairwise deplete them generating an epistatic interaction map, evaluate cell cycle perturbations upon knockdown in normal and cancer cells, and analyse their protein and mRNA expression in normal and cancer tissues. Using a novel FUSION algorithm, we integrate all data creating a comprehensive NUDIX enzyme profile map, which will prove fundamental to understanding their biological functionality.

  • 10.
    Chang, Tsung-Yao
    et al.
    Massachusetts Institute of Technology, USA.
    Pardo-Martin, Carlos
    Massachusetts Institute of Technology, USA.
    Allalou, Amin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Yanik, Mehmet Fatih
    Massachusetts Institute of Technology, USA.
    Fully automated cellular-resolution vertebrate screening platform with parallel animal processing2012In: Lab on a Chip, ISSN 1473-0197, E-ISSN 1473-0189, Vol. 12, no 4, 711-716 p.Article in journal (Refereed)
    Abstract [en]

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

  • 11.
    Clausson, Carl-Magnus
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Allalou, Amin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Weibrecht, Irene
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Mahmoudi, Salah
    Farnebo, Marianne
    Landegren, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Söderberg, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Increasing the dynamic range of in situ PLA2011In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 8, no 11, 892-893 p.Article in journal (Refereed)
  • 12. 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)
  • 13.
    Clausson, Carl-Magnus
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Söderberg, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Arngården, Linda
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Ishaq, Omer
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Nilsson, Mats
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Krzywkowski, Tomasz
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Compaction of rolling circle amplification products increases signal strength and integrityManuscript (preprint) (Other academic)
  • 14. Degerman, Johan
    et al.
    Althoff, Karin
    Thorlin, Thorleif
    Wählby, Carolina
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Karlsson, Patrick
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Gustavsson, Tomas
    Modeling stem cell migration by Hidden Markov2004In: Proceedings of the Swedish Symposium on Image Analysis, SSBA 2004, 2004, 122-125 p.Conference paper (Other scientific)
  • 15. Erlandsson, Fredrik
    et al.
    Wählby, Carolina
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Zetterberg, Anders
    University Administration. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Detection of large numbers of antigens using sequential immunofluorescence staining2001In: 7th European Society for Analytical Cellular Pathology Congress (ESACP 2001), Caen, France, 2001, 56-57 p.Conference paper (Other scientific)
  • 16.
    Erlandsson, Fredrik
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Ekholm-Reed, Susanna
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Hellström, Ann-Cathrin
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Zetterberg, Anders
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Abnormal expression pattern of cyclin E in tumour cells2003In: Int J Cancer, ISSN 0020-7136, Vol. 104, 369-375 p.Article in journal (Refereed)
  • 17.
    Erlandsson, Fredrik
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby (nee Linnman), Carolina
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Ekholm, Susanna
    Bengtsson, Ewert
    Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Zetterberg, Anders
    A detailed analysis of cyclin A accumulation at the G1/S border in normal and transformed cells.2000In: Experimental Cell Research, ISSN 0014-4827/00, Vol. 256, 86-95 p.Article in journal (Refereed)
    Abstract [en]

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

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

    more challenge. A new combination of image analysis algorithms for

    segmentation of cells imaged by fluorescence microscopy is presented. The

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

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

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

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

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

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

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

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

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

    Automatic cytoplasm segmentation was tested on CHO-cells stained with

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

    segmentation as compared to manual segmentation.

  • 18.
    Gavrilovic, Milan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Azar, Jimmy
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Busch, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology.
    Carlbom, Ingrid
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Blind Color Decomposition of Histological Images2013In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 32, no 6, 983-994 p.Article in journal (Refereed)
    Abstract [en]

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

  • 19.
    Gavrilovic, Milan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Weibrecht, Irene
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Conze, Tim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Söderberg, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Automated Classification of Multicolored Rolling Circle Products in Dual-Channel Wide-Field Fluorescence Microscopy2011In: Cytometry Part A, ISSN 1552-4922, Vol. 79A, no 7, 518-527 p.Article in journal (Refereed)
    Abstract [en]

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

  • 20.
    Gavrilovic, Milan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Algorithms for cross-talk suppression in fluorescence microscopy2008In: Medicinteknikdagarna 2008, 2008, 64-64 p.Conference paper (Other academic)
    Abstract [en]

     

     

     

  • 21. Gibbs, Anna
    et al.
    Buggert, Marcus
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Introini, Andrea
    Cheuk, Stanley
    Eidsmo, Liv
    Hirbod, Taha
    Ball, Terry B.
    Kimani, Joshua
    Kaul, Rupert
    Karlsson, Annika C.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Broliden, Kristina
    Tjernlund, Annelie
    Analysis of the distribution of CD103 on CD8 T cells in blood and genital mucosa of HIV-infected female sex workers2016In: AIDS Research and Human Retroviruses, ISSN 0889-2229, E-ISSN 1931-8405, Vol. 32, no S1, 307-307 p.Article in journal (Refereed)
  • 22.
    Holting, Per
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Easy-to-use object selection by color space projections and watershed segmentation2005In: Image Analysis and Processing: ICIAP 2005 13th International Conference, Cagliari, Italy, September 6-8, 2005. Proceedings, 2005, 269-276 p.Conference paper (Refereed)
    Abstract [en]

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

  • 23.
    Ishaq, Omer
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Elf, Johan
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    An Evaluation of the Faster STORM Method for Super-resolution Microscopy2014In: Proceedings of the 22nd International Conference on Pattern Recognition, 2014, 4435-4440 p.Conference paper (Refereed)
    Abstract [en]

    Development of new stochastic super-resolution methods together with fluorescence microscopy imaging enables visualization of biological processes at increasing spatial and temporal resolution. Quantitative evaluation of such imaging experiments call for computational analysis methods that localize the signals with high precision and recall. Furthermore, it is desirable that the methods are fast and possible to parallelize so that the ever increasing amounts of collected data can be handled in an efficient way. We here in address signal detection in super-resolution microscopy by approaches based on compressed sensing. We describe how a previously published approach can be parallelized, reducing processing time at least four times. We also evaluate the effect of a greedy optimization approach on signal recovery at high noise and molecule density. Furthermore, our evaluation reveals how previously published compressed sensing algorithms have a performance that degrades to that of a random signal detector at high molecule density. Finally, we show the approximation of the imaging system's point spread function affects recall and precision of signal detection, illustrating the importance of parameter optimization. We evaluate the methods on synthetic data with varying signal to noise ratio and increasing molecular density, and visualize performance on realsuper-resolution microscopy data from a time-lapse sequence of livingcells.

  • 24.
    Ishaq, Omer
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Negri, Joseph
    Bray, Mark-Anthony
    Pacureanu, Alexandra
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Peterson, Randall T.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Automated quantification of Zebrafish tail deformation for high-throughput drug screening2013In: Proc. 10th International Symposium on Biomedical Imaging: From Nano to Macro, Piscataway, NJ: IEEE , 2013, 902-905 p.Conference paper (Refereed)
  • 25.
    Ishaq, Omer
    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.
    Negri, Joseph
    Broad Institute of Harvard and MIT.
    Bray, Mark-Anthony
    Broad Institute of Harvard and MIT.
    Pacureanu, Alexandra
    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. Uppsala University, Science for Life Laboratory, SciLifeLab.
    An image based high-throughput assay for chemical screening using zebrafish.2012Conference paper (Refereed)
  • 26.
    Ishaq, Omer
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Sadanandan, Sajith Kecheril
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Deep Fish: Deep Learning-Based Classification of Zebrafish Deformation for High-Throughput Screening2017In: Journal of Biomolecular Screening, ISSN 1087-0571, E-ISSN 1552-454X, Vol. 22, no 1, 102-107 p.Article in journal (Refereed)
    Abstract [en]

    Zebrafish (Danio rerio) is an important vertebrate model organism in biomedical research, especially suitable for morphological screening due to its transparent body during early development. Deep learning has emerged as a dominant paradigm for data analysis and found a number of applications in computer vision and image analysis. Here we demonstrate the potential of a deep learning approach for accurate high-throughput classification of whole-body zebrafish deformations in multifish microwell plates. Deep learning uses the raw image data as an input, without the need of expert knowledge for feature design or optimization of the segmentation parameters. We trained the deep learning classifier on as few as 84 images (before data augmentation) and achieved a classification accuracy of 92.8% on an unseen test data set that is comparable to the previous state of the art (95%) based on user-specified segmentation and deformation metrics. Ablation studies by digitally removing whole fish or parts of the fish from the images revealed that the classifier learned discriminative features from the image foreground, and we observed that the deformations of the head region, rather than the visually apparent bent tail, were more important for good classification performance.

  • 27.
    Issac Niwas, Swamidoss
    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.
    Kårsnäs, Andreas
    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.
    Uhlmann, Virginie
    Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts MA, USA and Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland.
    Palanisamy, P.
    Dept. of Electronics and Communication Engineering (ECE), National Institute of Technology (NIT), Tiruchirappalli, India.
    Kampf, Caroline
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology.
    Simonsson, Martin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    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.
    Automated classification of immunostaining patterns in breast tissue from the Human Protein Atlas2012In: Histopathology Image Analysis (HIMA): a MICCAI 2012 workshop, 2012Conference paper (Refereed)
    Abstract [en]

    Background:

    The Human Protein Atlas (HPA) is an effort to map the location of all human proteins (http://www.proteinatlas.org/ ). It contains a large number of histological images of sections from human tissue. Tissue micro arrays are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples.

    Methods and Material:

    The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM) features, complex wavelet co-occurrence matrix (CWCM) features and WND-CHARM-inspired features. The extracted features are used into two different multivariate classifiers (SVM and LDA classifier). Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue.

    Results:

    Good results have been obtained by using the combinations of GLCM and wavelets and texture features, edge features, histograms, transforms, etc. (WND-CHARM). The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert.

    Conclusions:

    Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for quantification of staining patterns in histopathology have many applications, ranging from antibody quality control to tumour grading.

  • 28.
    Issac Niwas, Swamidoss
    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.
    Kårsnäs, Andreas
    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.
    Uhlmann, Virginie
    Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts MA, USA and Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland.
    Ponnusamy, Palanisamy
    Dept. of Electronics and Communication Engineering (ECE), National Institute of Technology (NIT), Tiruchirappalli, India.
    Kampf, Caroline
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology, Molecular and Morphological Pathology.
    Simonsson, Martin
    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, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Broad Institute of Harvard and Massachusetts Institute Technology (MIT), Cambridge, Massachusetts, MA, USA, .
    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.
    Automated classification of immunostaining patterns in breast tissue from the Human Protein Atlas2013In: Journal of Pathology Informatics, ISSN 2229-5089, E-ISSN 2153-3539, Vol. 4, no 14Article in journal (Refereed)
    Abstract [en]

    Background:

    The Human Protein Atlas (HPA) is an effort to map the location of all human proteins (http://www.proteinatlas.org/). It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA) are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples.

    Materials and Methods:

    The proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM) features, complex wavelet co-occurrence matrix (CWCM) features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM)-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM) and linear discriminant analysis (LDA) classifier). Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue.

    Results:

    We present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert.

    Conclusions:

    Both human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for quantification of staining patterns in histopathology have many applications, ranging from antibody quality control to tumor grading.

  • 29. Jahangir Tafrechi, Roshan S.
    et al.
    van de Rijke, Frans M.
    Allalou, Amin
    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.
    Larsson, Chatarina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Sloos, Willem C. R.
    van de Sande, Marchien
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Janssen, George M. C.
    Raap, Anton K.
    Single-cell A3243G mitochondrial DNA mutation load assays for segregation analysis2007In: Journal of Histochemistry and Cytochemistry, ISSN 0022-1554, E-ISSN 1551-5044, Vol. 55, no 11, 1159-1166 p.Article in journal (Refereed)
    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.

  • 30.
    Jarvius, Malin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Paulsson, Janna
    Weibrecht, Irene
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Leuchowius, Karl-Johan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Andersson, Ann-Catrin
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Gullberg, Mats
    Botling, Johan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Sjöblom, Tobias
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Markova, Boyka
    Östman, Arne
    Landegren, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Söderberg, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    In situ detection of phosphorylated platelet-derived growth factor receptor beta using a generalized proximity ligation method2007In: Molecular & Cellular Proteomics, ISSN 1535-9476, E-ISSN 1535-9484, Vol. 6, no 9, 1500-1509 p.Article in journal (Refereed)
    Abstract [en]

    Improved methods are needed for in situ characterization of post-translational modifications in cell lines and tissues. For example, it is desirable to monitor the phosphorylation status of individual receptor tyrosine kinases in samples from human tumors treated with inhibitors to evaluate therapeutic responses. Unfortunately the leading methods for observing the dynamics of tissue post-translational modifications in situ, immunohistochemistry and immunofluorescence, exhibit limited sensitivity and selectivity. Proximity ligation assay is a novel method that offers improved selectivity through the requirement of dual recognition and increased sensitivity by including DNA amplification as a component of detection of the target molecule. Here we therefore established a generalized in situ proximity ligation assay to investigate phosphorylation of platelet-derived growth factor receptor β (PDGFRβ) in cells stimulated with platelet-derived growth factor BB. Antibodies specific for immunoglobulins from different species, modified by attachment of DNA strands, were used as secondary proximity probes together with a pair of primary antibodies from the corresponding species. Dual recognition of receptors and phosphorylated sites by the primary antibodies in combination with the secondary proximity probes was used to generate circular DNA strands; this was followed by signal amplification by replicating the DNA circles via rolling circle amplification. We detected tyrosine phosphorylated PDGFRβ in human embryonic kidney cells stably overexpressing human influenza hemagglutinin-tagged human PDGFRβ in porcine aortic endothelial cells transfected with the β-receptor, but not in cells transfected with the α-receptor, and also in immortalized human foreskin fibroblasts, BJ hTert, endogenously expressing the PDGFRβ. We furthermore visualized tyrosine phosphorylated PDGFRβ in tissue sections from fresh frozen human scar tissue undergoing wound healing. The method should be of great value to study signal transduction, screen for effects of pharmacological agents, and enhance the diagnostic potential in histopathology.

  • 31.
    Karlsson, Patrick
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Lindblad, Joakim
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Segmentation of point-like fluorescent markers2004In: Proceedings: Symposium on Image Analysis, 2004, 202- p.Conference paper (Other scientific)
    Abstract [en]

    We present a method for accurate segmentation of point like signals, from fluorescent markers in digital microscopic images with subcellular resolution. The method is able to segment and separate clustered signals, which facilitates accurate dot counting. The method performance is evaluated using synthetic images, that are modeled after real digital microscopy images of cells. The results show that the method is able to detect point like fluorescent signals as correct as a manual operator.

  • 32.
    Ke, Rongqin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Mignardi, Marco
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Pacureanu, Alexandra
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Svedlund, Jessica
    Botling, Johan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology.
    Wählby, Carolina
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Nilsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools.
    In situ sequencing for RNA analysis in preserved tissue and cells2013In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 10, no 9, 857-860 p.Article in journal (Refereed)
    Abstract [en]

    Tissue gene expression profiling is performed on homogenates or on populations of isolated single cells to resolve molecular states of different cell types. In both approaches, histological context is lost. We have developed an in situ sequencing method for parallel targeted analysis of short RNA fragments in morphologically preserved cells and tissue. We demonstrate in situ sequencing of point mutations and multiplexed gene expression profiling in human breast cancer tissue sections.

  • 33.
    Kecheril Sadanandan, Sajith
    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.
    Baltekin, Özden
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
    Magnusson, Klas E. G.
    Boucharin, Alexis
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
    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.
    Jaldén, Joakim
    Elf, Johan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Segmentation and track-analysis in time-lapse imaging of bacteria2016In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 10, no 1, 174-184 p.Article in journal (Refereed)
    Abstract [en]

    In this paper, we have developed tools to analyze prokaryotic cells growing in monolayers in a microfluidic device. Individual bacterial cells are identified using a novel curvature based approach and tracked over time for several generations. The resulting tracks are thereafter assessed and filtered based on track quality for subsequent analysis of bacterial growth rates. The proposed method performs comparable to the state-of-the-art methods for segmenting phase contrast and fluorescent images, and we show a 10-fold increase in analysis speed.

  • 34.
    Kecheril Sadanandan, Sajith
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Karlsson, Johan
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Spheroid segmentation using multiscale deep adversarial networks2017In: IEEE International Conference on Computer Vision, 2017Conference paper (Refereed)
  • 35.
    Kecheril Sadanandan, Sajith
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Le Guyader, Sylvie
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Automated training of deep convolutional neural networks for cell segmentation2017In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, 7860Article in journal (Refereed)
  • 36.
    Kecheril Sadanandan, Sajith
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    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, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Feature augmented deep neural networks for segmentation of cells2016In: Computer Vision – ECCV 2016 Workshops: Part I, Springer, 2016, 231-243 p.Conference paper (Refereed)
  • 37.
    Kecheril Sadanandan, Sajith
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Large-Scale Analysis of Live Cells2013Conference paper (Other (popular science, discussion, etc.))
  • 38. Khorshidi, Mohammad Ali
    et al.
    Rajeswari, Prem Kumar Periyannan
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Jönsson, Håkan N.
    Andersson Svahn, Helene
    Automated analysis of dynamic behavior of single cells in picoliter droplets2014In: Lab on a Chip, ISSN 1473-0197, E-ISSN 1473-0189, Vol. 14, 931-937 p.Article in journal (Refereed)
  • 39. Kirienko, Natalia V.
    et al.
    Kirienko, Daniel R.
    Larkins-Ford, Jonah
    Wählby, Carolina
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Ruvkun, Gary
    Ausubel, Frederick M.
    Pseudomonas aeruginosa Disrupts Caenorhabditis elegans Iron Homeostasis, Causing a Hypoxic Response and Death2013In: Cell Host and Microbe, ISSN 1931-3128, E-ISSN 1934-6069, Vol. 13, no 4, 406-416 p.Article in journal (Refereed)
    Abstract [en]

    The opportunistic pathogen Pseudomonas aeruginosa causes serious human infections, but effective treatments and the mechanisms mediating pathogenesis remain elusive. Caenorhabditis elegans shares innate immune pathways with humans, making it invaluable to investigate infection. To determine how P. aeruginosa disrupts host biology, we studied how P. aeruginosa kills C. elegans in a liquid-based pathogenesis model. We found that P. aeruginosa-mediated killing does not require quorum-sensing pathways or host colonization. A chemical genetic screen revealed that iron chelators alleviate P. aeruginosa-mediated killing. Consistent with a role for iron in P. aeruginosa pathogenesis, the bacterial siderophore pyoverdin was required for virulence and was sufficient to induce a hypoxic response and death in the absence of bacteria. Loss of the C. elegans hypoxia-inducing factor HIF-1, which regulates iron homeostasis, exacerbated P. aeruginosa pathogenesis, further linking hypoxia and killing. As pyoverdin is indispensable for virulence in mice, pyoverdin-mediated hypoxia is likely to be relevant in human pathogenesis.

  • 40.
    Koos, Björn
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Kamali-Moghaddam, Masood
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab.
    David, Leonor
    Sobrinho-Simoes, Manuel
    Dimberg, Anna
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    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. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Next-Generation Pathology: Surveillance of Tumor Microecology2015In: Journal of Molecular Biology, ISSN 0022-2836, E-ISSN 1089-8638, Vol. 427, no 11, 2013-2022 p.Article, review/survey (Refereed)
    Abstract [en]

    A tumor is a heterogeneous population of cells that provides an environment in which every cell resides in a microenvironmental niche. Microscopic evaluation of tissue sections, based on histology and immunohistochemistry, has been a cornerstone in pathology for decades. However, the dawn of novel technologies to investigate genetic aberrations is currently adopted in routine molecular pathology. We herein describe our view on how recent developments in molecular technologies, focusing on proximity ligation assay and padlock probes, can be applied to merge the two branches of pathology, allowing molecular profiling under histologic observation. We also discuss how the use of image analysis will be pivotal to obtain information at a cellular level and to interpret holistic images of tissue sections. By understanding the cellular communications in the microecology of tumors, we will be at a better position to predict disease progression and response to therapy.

  • 41.
    Lindblad, Joakim
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Zaltsman, Alla
    Image Analysis for Automatic Segmentation of Cytoplasms and Classification of Rac1 Activation2004In: Cytometry, ISSN 0196-4763, E-ISSN 1097-0320, Vol. 57A, no 1, 22-23 p.Article in journal (Refereed)
    Abstract [en]

    BACKGROUND:

    Rac1 is a GTP-binding molecule involved in a wide range of cellular processes. Using digital image analysis, agonist-induced translocation of green fluorescent protein (GFP) Rac1 to the cellular membrane can be estimated quantitatively for individual cells.

    METHODS:

    A fully automatic image analysis method for cell segmentation, feature extraction, and classification of cells according to their activation, i.e., GFP-Rac1 translocation and ruffle formation at stimuli, is described. Based on training data produced by visual annotation of four image series, a statistical classifier was created.

    RESULTS:

    The results of the automatic classification were compared with results from visual inspection of the same time sequences. The automatic classification differed from the visual classification at about the same level as visual classifications performed by two different skilled professionals differed from each other. Classification of a second image set, consisting of seven image series with different concentrations of agonist, showed that the classifier could detect an increased proportion of activated cells at increased agonist concentration.

    CONCLUSIONS:

    Intracellular activities, such as ruffle formation, can be quantified by fully automatic image analysis, with an accuracy comparable to that achieved by visual inspection. This analysis can be done at a speed of hundreds of cells per second and without the subjectivity introduced by manual judgments.

  • 42.
    Lindblad, Joakim
    et al.
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wählby, Carolina
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Vondrus, Mikael
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Björkesten, Lennart
    Statistical quality control for segmentation of fluorescence labelled cells2001In: 5th Korea-Germany Joint Workshop on Advanced Medical Image Processing, Seoul, Korea, 2001Conference paper (Other scientific)
  • 43.
    Matuszewski, Damian J.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Science for Life Laboratory, Uppsala, Sweden.
    Hast, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    A short feature vector for image matching: The Log-Polar Magnitude feature descriptor2017In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 11, e0188496Article in journal (Refereed)
    Abstract [en]

    The choice of an optimal feature detector-descriptor combination for image matching often depends on the application and the image type. In this paper, we propose the Log-Polar Magnitude feature descriptor—a rotation, scale, and illumination invariant descriptor that achieves comparable performance to SIFT on a large variety of image registration problems but with much shorter feature vectors. The descriptor is based on the Log-Polar Transform followed by a Fourier Transform and selection of the magnitude spectrum components. Selecting different frequency components allows optimizing for image patterns specific for a particular application. In addition, by relying only on coordinates of the found features and (optionally) feature sizes our descriptor is completely detector independent. We propose 48- or 56-long feature vectors that potentially can be shortened even further depending on the application. Shorter feature vectors result in better memory usage and faster matching. This combined with the fact that the descriptor does not require a time-consuming feature orientation estimation (the rotation invariance is achieved solely by using the magnitude spectrum of the Log-Polar Transform) makes it particularly attractive to applications with limited hardware capacity. Evaluation is performed on the standard Oxford dataset and two different microscopy datasets; one with fluorescence and one with transmission electron microscopy images. Our method performs better than SURF and comparable to SIFT on the Oxford dataset, and better than SIFT on both microscopy datasets indicating that it is particularly useful in applications with microscopy images.

  • 44.
    Matuszewski, Damian J.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Puigvert, Jordi Carreras
    Uppsala University, Science for Life Laboratory, SciLifeLab. Division of Translational Medicine and Chemical Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Comparison of Flow Cytometry and Image-Based Screening for Cell Cycle Analysis2016In: Image Analysis And Recognition (ICIAR 2016) / [ed] Aurélio Campilho, Fakhri Karray, Springer, 2016, Vol. 9730, 623-630 p.Conference paper (Refereed)
    Abstract [en]

    Quantitative cell state measurements can provide a wealth of information about mechanism of action of chemical compounds and gene functionality. Here we present a comparison of cell cycle disruption measurements from commonly used flow cytometry (generating onedimensional signal data) and bioimaging (producing two-dimensional image data). Our results show high correlation between the two approaches indicating that image-based screening can be used as an alternative to flow cytometry. Furthermore, we discuss the benefits of image informatics over conventional single-signal flow cytometry.

  • 45.
    Matuszewski, Damian J.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Carreras Puigvert, Jordi
    Karolinska Inst, Dept Med Biochem & Biophys, Div Translat Med & Chem Biol, Stockholm, Sweden; ] Sci Life Lab, Stockholm, Sweden .
    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. Uppsala University, Science for Life Laboratory, SciLifeLab.
    PopulationProfiler: A Tool for Population Analysis and Visualization of Image-Based Cell Screening Data2016In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 3, e0151554Article in journal (Refereed)
    Abstract [en]

    Image-based screening typically produces quantitative measurements of cell appearance. Large-scale screens involving tens of thousands of images, each containing hundreds of cells described by hundreds of measurements, result in overwhelming amounts of data. Reducing per-cell measurements to the averages across the image(s) for each treatment leads to loss of potentially valuable information on population variability. We present PopulationProfiler-a new software tool that reduces per-cell measurements to population statistics. The software imports measurements from a simple text file, visualizes population distributions in a compact and comprehensive way, and can create gates for subpopulation classes based on control samples. We validate the tool by showing how PopulationProfiler can be used to analyze the effect of drugs that disturb the cell cycle, and compare the results to those obtained with flow cytometry.

  • 46.
    Mignardi, Marco
    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.
    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.
    Qian, Xiaoyan
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, S-17165 Stockholm, Sweden..
    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.
    Bridging Histology and Bioinformatics: Computational analysis of spatially resolved transcriptomics2017In: Proceedings of the IEEE, ISSN 0018-9219, E-ISSN 1558-2256, Vol. 105, no 3, 530-541 p.Article in journal (Refereed)
    Abstract [en]

    It is well known that cells in tissue display a large heterogeneity in gene expression due to differences in cell lineage origin and variation in the local environment. Traditional methods that analyze gene expression from bulk RNA extracts fail to accurately describe this heterogeneity because of their intrinsic limitation in cellular and spatial resolution. Also, information on histology in the form of tissue architecture and organization is lost in the process. Recently, new transcriptome-wide analysis technologies have enabled the study of RNA molecules directly in tissue samples, thus maintaining spatial resolution and complementing histological information with molecular information important for the understanding of many biological processes and potentially relevant for the clinical management of cancer patients. These new methods generally comprise three levels of analysis. At the first level, biochemical techniques are used to generate signals that can be imaged by different means of fluorescence microscopy. At the second level, images are subject to digital image processing and analysis in order to detect and identify the aforementioned signals. At the third level, the collected data are analyzed and transformed into interpretable information by statistical methods and visualization techniques relating them to each other, to spatial distribution, and to tissue morphology. In this review, we describe state-of-the-art techniques used at all three levels of analysis. Finally, we discuss future perspective in this fast-growing field of spatially resolved transcriptomics.

  • 47.
    Pacureanu, Alexandra
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Peyrin, Francoise
    European Synchrotron Radiation Facility, Creatis, Université de Lyon .
    Langer, Max
    European Synchrotron Radiation Facility, Creatis, Université de Lyon .
    Ishaq, Omer
    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.
    Allalou, Amin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Isotropic 3D imaging of biological specimens at micro and nano scale2012Conference paper (Other academic)
  • 48. Pardo-Martin, Carlos
    et al.
    Allalou, Amin
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Medina, Jaime
    Eimon, Peter M.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Yanik, Mehmet Fatih
    High-throughput hyperdimensional vertebrate phenotyping2013In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 4, 1467- p.Article in journal (Refereed)
    Abstract [en]

    Most gene mutations and biologically active molecules cause complex responses in animals that cannot be predicted by cell culture models. Yet animal studies remain too slow and their analyses are often limited to only a few readouts. Here we demonstrate high-throughput optical projection tomography with micrometre resolution and hyperdimensional screening of entire vertebrates in tens of seconds using a simple fluidic system. Hundreds of independent morphological features and complex phenotypes are automatically captured in three dimensions with unprecedented speed and detail in semitransparent zebrafish larvae. By clustering quantitative phenotypic signatures, we can detect and classify even subtle alterations in many biological processes simultaneously. We term our approach hyperdimensional in vivo phenotyping. To illustrate the power of hyperdimensional in vivo phenotyping, we have analysed the effects of several classes of teratogens on cartilage formation using 200 independent morphological measurements, and identified similarities and differences that correlate well with their known mechanisms of actions in mammals.

  • 49. Pardo-Martin, Carlos
    et al.
    Chang, Tsung-Yao
    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.
    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.
    Yanik, Mehmet Fatih
    High-throughput cellular-resolution in vivo vertebrate screening2011In: Proc. 15th International Conference on Miniaturized Systems for Chemistry and Life Sciences, 2011Conference paper (Refereed)
  • 50.
    Pinidiyaarachchi, Amalka
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Göransson, Jenny
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Gonzalez-Rey, Carlos
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Howell, Mathias
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Melin, Jonas
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences.
    Jarvius, Jonas
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Nilsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Digital image processing for multiplexing of single molecule detection2005In: Medicinteknikdagarna: Stockholm/Södertälje September 27-28, 2005, 2005Conference paper (Other academic)
    Abstract [en]

    Using padlock and proximity probing techniques, individual molecular identification events are converted to long DNA molecules, carrying repeated sequence motifs used for identification of the detected molecules. We show that identification events can be amplified using rolling circle replication, and randomly attached to a surface for repeated access by identification probes. Repeated hybridization with detection probes carrying fluorescing nano-crystals (quantum dots) of varying spectral properties opens the possibility to search for large numbers of different identification events simultaneously. Methods for digital image processing of the resulting multi-spectral data include spatial as well as spectral data clustering. Spatial data processing includes registration of images from repeated hybridization events as well as delineation of clustered reporter events. Spectral data processing and analysis includes classification of spectral data into groups of either pre-defined or unknown patterns representing different molecular identification events.

12 1 - 50 of 85
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