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

IF 1.7 4区计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

IEEE Computer Graphics and Applications Pub Date : 2024-01-01 DOI:10.1109/MCG.2023.3333475

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

引用次数: 0

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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可视化评估医学图像分割中观察者间差异性的工作流程。

我们介绍了一种可视化评估医学影像分割中观察者间差异性的工作流程。图像分割是许多疾病诊断、预后和治疗的关键步骤。尽管自动分割技术不断进步，但临床实践仍广泛依赖于放射科医生的手动分割。我们的工作重点是设计一种解决方案，以了解放射科医生在分割过程中的思维过程，并揭示导致观察者之间差异的原因。为此，我们提出了一种可视化分析工具，将多位放射科医生的划线过程与结果联系起来，并通过案例研究证明了这一工具的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Computer Graphics and Applications 工程技术-计算机：软件工程

CiteScore

3.20

自引率

5.60%

发文量

160

审稿时长

>12 weeks

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