VCBM 2022: Eurographics Workshop on Visual Computing for Biology and Medicine

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https://diglib.eg.org/handle/10.2312/2633211

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Browsing VCBM 2022: Eurographics Workshop on Visual Computing for Biology and Medicine by Subject "Applied computing"

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    MuSIC: Multi-Sequential Interactive Co-Registration for Cancer Imaging Data based on Segmentation Masks
    (The Eurographics Association, 2022) Eichner, Tanja; Mörth, Eric; Wagner-Larsen, Kari S.; Lura, Njål; Haldorsen, Ingfrid S.; Gröller, Eduard; Bruckner, Stefan; Smit, Noeska N.; Renata G. Raidou; Björn Sommer; Torsten W. Kuhlen; Michael Krone; Thomas Schultz; Hsiang-Yun Wu
    In gynecologic cancer imaging, multiple magnetic resonance imaging (MRI) sequences are acquired per patient to reveal different tissue characteristics. However, after image acquisition, the anatomical structures can be misaligned in the various sequences due to changing patient location in the scanner and organ movements. The co-registration process aims to align the sequences to allow for multi-sequential tumor imaging analysis. However, automatic co-registration often leads to unsatisfying results. To address this problem, we propose the web-based application MuSIC (Multi-Sequential Interactive Co-registration). The approach allows medical experts to co-register multiple sequences simultaneously based on a pre-defined segmentation mask generated for one of the sequences. Our contributions lie in our proposed workflow. First, a shape matching algorithm based on dual annealing searches for the tumor position in each sequence. The user can then interactively adapt the proposed segmentation positions if needed. During this procedure, we include a multi-modal magic lens visualization for visual quality assessment. Then, we register the volumes based on the segmentation mask positions. We allow for both rigid and deformable registration. Finally, we conducted a usability analysis with seven medical and machine learning experts to verify the utility of our approach. Our participants highly appreciate the multi-sequential setup and see themselves using MuSIC in the future.
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    Perceptual Evaluation of Common Line Variables for Displaying Uncertainty on Molecular Surfaces
    (The Eurographics Association, 2022) Sterzik, Anna; Lichtenberg, Nils; Krone, Michael; Cunningham, Douglas W.; Lawonn, Kai; Renata G. Raidou; Björn Sommer; Torsten W. Kuhlen; Michael Krone; Thomas Schultz; Hsiang-Yun Wu
    Data are often subject to some degree of uncertainty, whether aleatory or epistemic. This applies both to experimental data acquired with sensors as well as to simulation data. Displaying these data and their uncertainty faithfully is crucial for gaining knowledge. Specifically, the effective communication of the uncertainty can influence the interpretation of the data and the users' trust in the visualization. However, uncertainty-aware visualization has gotten little attention in molecular visualization. When using the established molecular representations, the physicochemical attributes of the molecular data usually already occupy the common visual channels like shape, size, and color. Consequently, to encode uncertainty information, we need to open up another channel by using feature lines. Even though various line variables have been proposed for uncertainty visualizations, they have so far been primarily used for two-dimensional data and there has been little perceptual evaluation. Therefore, we conducted a perceptual study to determine the suitability of the line variables sketchiness, dashing, grayscale, and width for distinguishing several uncertainty values on molecular surfaces.