Bayat, H. C., Waldner, M., & Raidou, R. G. (2024). A Workflow to Visually Assess Interobserver Variability in Medical Image Segmentation. IEEE Computer Graphics and Applications, 44(1), 86–94. https://doi.org/10.1109/MCG.2023.3333475
E193-02 - Forschungsbereich Computer Graphics E056-18 - Fachbereich Visual Analytics and Computer Vision Meet Cultural Heritage
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Journal:
IEEE Computer Graphics and Applications
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ISSN:
0272-1716
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Date (published):
Jan-2024
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Number of Pages:
9
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Publisher:
IEEE COMPUTER SOC
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Peer reviewed:
Yes
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Keywords:
Humans; Observer Variation; Workflow; Algorithms
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Abstract:
We introduce a workflow for the visual assessment of interobserver variability in medical image segmentation. Image segmentation is a crucial step in the diagnosis, prognosis, and treatment of many diseases. Despite the advancements in autosegmentation, clinical practice widely relies on manual delineations performed by radiologists. Our work focuses on designing a solution for understanding the radiologists' thought processes during segmentation and for unveiling reasons that lead to interobserver variability. To this end, we propose a visual analysis tool connecting multiple radiologists' delineation processes with their outcomes, and we demonstrate its potential in a case study.
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Research Areas:
Visual Computing and Human-Centered Technology: 100%
