Hannah Clara Bayat, Manuela Waldner, Renata G Raidou, Mike Potel
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A Workflow to Visually Assess Interobserver Variability in Medical Image Segmentation.
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.
期刊介绍:
IEEE Computer Graphics and Applications (CG&A) bridges the theory and practice of computer graphics, visualization, virtual and augmented reality, and HCI. From specific algorithms to full system implementations, CG&A offers a unique combination of peer-reviewed feature articles and informal departments. Theme issues guest edited by leading researchers in their fields track the latest developments and trends in computer-generated graphical content, while tutorials and surveys provide a broad overview of interesting and timely topics. Regular departments further explore the core areas of graphics as well as extend into topics such as usability, education, history, and opinion. Each issue, the story of our cover focuses on creative applications of the technology by an artist or designer. Published six times a year, CG&A is indispensable reading for people working at the leading edge of computer-generated graphics technology and its applications in everything from business to the arts.