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 → Health informatics"

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    Clusters in Focus: A Simple and Robust Detail-On-Demand Dashboard for Patient Data
    (The Eurographics Association, 2025) Schilcher, Lukas; Waldert, Peter; Kantz, Benedikt; Schreck, Tobias; Garrison, Laura; Krueger, Robert
    Exploring tabular datasets to understand how different feature pairs partition data into meaningful cohorts is crucial in domains such as biomarker discovery, yet comparing clusters across multiple feature pair projections is challenging. We introduce Clusters in Focus, an interactive visual analytics dashboard designed to address this gap. Clusters in Focus employs a threepanel coordinated view: a Data Panel offers multiple perspectives (tabular, heatmap, condensed with histograms / SHAP values) for initial data exploration; a Selection Panel displays the 2D clustering (K-Means/DBSCAN) for a user-selected feature pair; and a novel Cluster Similarity Panel featuring two switchable views for comparing clusters. A ranked list enables the identification of top-matching feature pairs, while an interactive similarity matrix with reordering capabilities allows for the discovery of global structural patterns and groups of related features. This dual-view design supports both focused querying and broad visual exploration. A use case on a Parkinson's disease speech dataset demonstrates the tool's effectiveness in revealing relationships between different feature pairs characterizing the same patient subgroup.
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    Workflow for AI-Supported Stenosis Prediction in X-Ray Coronary Angiography for SYNTAX Score Calculation
    (The Eurographics Association, 2024) Popp, Antonia; El Al, Alaa Abd; Hoffmann, Marie; Laube, Ann; Kempfert, Jörg; Hennemuth, Anja; Meyer, Alexander; Garrison, Laura; Jönsson, Daniel
    X-ray coronary angiography is the primary imaging modality for evaluating coronary artery disease. The visual assessment of angiography videos in clinical routines is time-consuming, requires expert experience and lacks standardization. This complicates the calculation of the SYNTAX score, a recommended instrument for therapy decision making. In this work we propose an end-to-end pipeline for segment-wise stenosis prediction in multi-view angiography videos to facilitate the calculation of the SYNTAX score. While recent approaches mainly focus on stenosis detection on frame- or video-level, our method is developed and evaluated for stenosis prediction on patient-level. The pipeline is composed as follows: (1) Selection of frames showing arteries filled with contrast medium using a convolutional neural network, (2) Stenosis detection and segment labelling on selected frames using a region-based convolutional neural network for object detection, (3) Linkage of detected regions showing the same stenosis by tracking the optical flow of the detections in the angiography video, (4) Segment assignment to the detected and tracked stenosis to predict stenotic segments on patient-level. The workflow is adjusted and evaluated using the image data and diagnostic annotations of 219 patients with multi-vessel coronary artery disease from the German Heart Center of the Charité University Hospital (DHZC), Berlin. To fine-tune the models, we used manually flagged frames for the frame classification model and bounding box annotations provided by a cardiac expert for the stenosis detection model. For the segment-wise prediction of all patients, we achieved a total sensitivity of 56.41, specificity of 85.88, precision of 52.81 and F1 score of 54.55 with varying results for the 25 coronary segments. The established workflow can facilitate visual assessment of CAD in angiography videos and increase accuracy and precision in clinical diagnostics.