A Workflow to Visually Assess Interobserver Variability in Medical Image Segmentation.

Hannah Clara Bayat,Manuela Waldner,Renata G Raidou, Mike Potel

IEEE computer graphics and applications（2024）

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摘要

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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关键词

Inter-rater,Medical Imaging,Image Segmentation,Medical Image Segmentation,Segmentation Variables,Interaction Analysis,Visual Analysis,Segmentation Task,Uncertainty Quantification,Reasonable Strategy,Reasoning Process,Treatment Of Many Diseases,Manual Delineation,Automatic Segmentation Algorithm,Medical World,Data Acquisition Protocol,False Negative,True Positive,Medical Students,Utterances,Candidate Words,Think-aloud Protocols,Mouse Movements,Part Of The Tumor,Eye-tracking Data,Visual Attention,Environment For Participants,Target Object,Eye-tracking,Domain Experts

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