可视化评估医学图像分割中观察者间差异性的工作流程。

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Hannah Clara Bayat, Manuela Waldner, Renata G Raidou, Mike Potel
{"title":"可视化评估医学图像分割中观察者间差异性的工作流程。","authors":"Hannah Clara Bayat, Manuela Waldner, Renata G Raidou, Mike Potel","doi":"10.1109/MCG.2023.3333475","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"44 1","pages":"86-94"},"PeriodicalIF":1.7000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Workflow to Visually Assess Interobserver Variability in Medical Image Segmentation.\",\"authors\":\"Hannah Clara Bayat, Manuela Waldner, Renata G Raidou, Mike Potel\",\"doi\":\"10.1109/MCG.2023.3333475\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":55026,\"journal\":{\"name\":\"IEEE Computer Graphics and Applications\",\"volume\":\"44 1\",\"pages\":\"86-94\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Computer Graphics and Applications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1109/MCG.2023.3333475\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Computer Graphics and Applications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/MCG.2023.3333475","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0

摘要

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。
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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
IEEE Computer Graphics and Applications
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
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信