<div class="csl-bib-body">
<div class="csl-entry">Bayat, H. C., Waldner, M., & Raidou, R. G. (2024). A Workflow to Visually Assess Interobserver Variability in Medical Image Segmentation. <i>IEEE Computer Graphics and Applications</i>, <i>44</i>(1), 86–94. https://doi.org/10.1109/MCG.2023.3333475</div>
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dc.identifier.issn
0272-1716
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/197494
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dc.description.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.
en
dc.language.iso
en
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dc.publisher
IEEE COMPUTER SOC
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dc.relation.ispartof
IEEE Computer Graphics and Applications
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dc.subject
Humans
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dc.subject
Observer Variation
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dc.subject
Workflow
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dc.subject
Algorithms
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dc.title
A Workflow to Visually Assess Interobserver Variability in Medical Image Segmentation
