VCBM: Eurographics Workshop on Visual Computing for Biomedicine

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Browsing VCBM: Eurographics Workshop on Visual Computing for Biomedicine by Subject "Applied computing → Imaging"

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    HistoContours: a Framework for Visual Annotation of Histopathology Whole Slide Images
    (The Eurographics Association, 2022) Al-Thelaya, Khaled; Joad, Faaiz; Gilal, Nauman Ullah; Mifsud, William; Pintore, Giovanni; Gobbetti, Enrico; Agus, Marco; Schneider, Jens; Renata G. Raidou; Björn Sommer; Torsten W. Kuhlen; Michael Krone; Thomas Schultz; Hsiang-Yun Wu
    We present an end-to-end framework for histopathological analysis of whole slide images (WSIs). Our framework uses deep learning-based localization & classification of cell nuclei followed by spatial data aggregation to propagate classes of sparsely distributed nuclei across the entire slide. We use YOLO (''You Only Look Once'') for localization instead of more costly segmentation approaches and show that using HistAuGAN boosts its performance. YOLO finds bounding boxes around nuclei at good accuracy, but the classification accuracy can be improved by other methods. To this end, we extract patches around nuclei from the WSI and consider models from the SqueezeNet, ResNet, and EfficientNet families for classification. Where we do not achieve a clear separation between highest and second-highest softmax activation of the classifier, we use YOLO's output as a secondary vote. The result is a sparse annotation of the WSI, which we turn dense by using kernel density estimation. The result is a full vector of per pixel probabilities for each class of nucleus we consider. This allows us to visualize our results using both color-coding and isocontouring, reducing visual clutter. Our novel nuclei-to-tissue coupling allows histopathologists to work at both the nucleus and the tissue level, a feature appreciated by domain experts in a qualitative user study.
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    Virtually Objective Quantification of in vitro Wound Healing Scratch Assays with the Segment Anything Model
    (The Eurographics Association, 2024) Löwenstein, Katja; Rehrl, Johanna; Schuster, Anja; Gadermayr, Michael; Garrison, Laura; Jönsson, Daniel
    The in vitro scratch assay is a widely used assay in cell biology to assess the rate of wound closure related to a variety of therapeutic interventions. While manual measurement is subjective and vulnerable to intra- and interobserver variability, computer-based tools are theoretically objective, but in practice often contain parameters which are manually adjusted (individually per image or data set) and thereby provide a source for subjectivity. Modern deep learning approaches typically require large annotated training data which complicates instant applicability. In this paper, we make use of the segment anything model, a deep foundation model based on interactive point-prompts, which enables class-agnostic segmentation without tuning the network's parameters based on domain specific training data. The proposed method clearly outperformed a semi-objective baseline method that required manual inspection and, if necessary, adjustment of parameters per image. Even though the point prompts of the proposed approach are theoretically also a source for subjectivity, results attested very low intra- and interobserver variability, even compared to manual segmentation of domain experts.