arXiv 2312.095762023-12-15eess.IVcs.CV

SegRap2023：面向鼻咽癌放疗规划的危及器官与肿瘤大体靶区分割基准

SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma

University of Electronic Science and Technology of China（电子科技大学）；Sichuan Cancer Hospital & Institute（四川省肿瘤医院（研究所））；Shanghai AI Lab（上海人工智能实验室）；Canon Medical Systems (China) Co. Ltd.（佳能医疗系统（中国）有限公司）；Stockholm University（斯德哥尔摩大学）；KTH（瑞典皇家理工学院）；Northwestern Polytechnical University（西北工业大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））

Xiangde Luo, Jia Fu, Yunxin Zhong, Shuolin Liu, Bing Han, Mehdi Astaraki, Simone Bendazzoli, Iuliana Toma-Dasu, Yiwen Ye, Ziyang Chen, Yong Xia, Yanzhou Su, Jin… 展开作者

Xiangde Luo, Jia Fu, Yunxin Zhong, Shuolin Liu, Bing Han, Mehdi Astaraki, Simone Bendazzoli, Iuliana Toma-Dasu, Yiwen Ye, Ziyang Chen, Yong Xia, Yanzhou Su, Jin Ye, Junjun He, Zhaohu Xing, Hongqiu Wang, Lei Zhu, Kaixiang Yang, Xin Fang, Zhiwei Wang, Chan Woong Lee, Sang Joon Park, Jaehee Chun, Constantin Ulrich, Klaus H. Maier-Hein, Nchongmaje Ndipenoch, Alina Miron, Yongmin Li, Yimeng Zhang, Yu Chen, Lu Bai, Jinlong Huang, Chengyang An, Lisheng Wang, Kaiwen Huang, Yunqi Gu, Tao Zhou, Mu Zhou, Shichuan Zhang, Wenjun Liao, Guotai Wang, Shaoting Zhang

备注：: A challenge report of SegRap2023 (organized in conjunction with MICCAI2023)

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AI总结：

针对鼻咽癌放疗中危及器官与肿瘤靶区分割公开数据集稀缺的问题，SegRap2023挑战赛联合MICCAI2023构建了含200例患者配对CT的大规模分割基准，评估参赛方案并指出小结构靶区与器官分割仍待突破。

摘要：

放射治疗是鼻咽癌（NasoPharyngeal Carcinoma，NPC）的主要有效治疗策略。精准勾画肿瘤大体靶区（Gross Tumor Volumes，GTVs）和危及器官（Organs-At-Risk，OARs）在放射治疗中至关重要，直接影响患者预后。此前，GTVs和OARs的勾画由经验丰富的放射肿瘤医师完成。近年来，深度学习在诸多医学图像分割任务中取得了可观成效，但针对鼻咽癌OARs和GTVs分割，可供模型开发与评估的公开数据集十分稀缺。为缓解这一问题，SegRap2023挑战赛与MICCAI2023联合举办，推出了一个大规模OAR和GTV分割基准，包含来自200名鼻咽癌患者的400例计算机断层扫描（Computed Tomography，CT）影像，每位患者均配准有一对平扫与增强CT扫描影像。该挑战赛的目标是从配对CT影像中分割出45个OARs和2个GTVs。本文详细介绍了本次挑战赛，并分析了所有参赛队伍的解决方案。所有提交方案的平均Dice相似系数得分，OARs为76.68%至86.70%，GTVs为70.42%至73.44%。我们得出结论：大尺寸OARs的分割问题已得到较好解决，而GTVs以及小尺寸或薄结构OARs的分割仍需更多研究投入。该基准将持续公开，访问地址为：https://segrap2023.grand-challenge.org

英文摘要：

Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis. Previously, the delineation of GTVs and OARs was performed by experienced radiation oncologists. Recently, deep learning has achieved promising results in many medical image segmentation tasks. However, for NPC OARs and GTVs segmentation, few public datasets are available for model development and evaluation. To alleviate this problem, the SegRap2023 challenge was organized in conjunction with MICCAI2023 and presented a large-scale benchmark for OAR and GTV segmentation with 400 Computed Tomography (CT) scans from 200 NPC patients, each with a pair of pre-aligned non-contrast and contrast-enhanced CT scans. The challenge's goal was to segment 45 OARs and 2 GTVs from the paired CT scans. In this paper, we detail the challenge and analyze the solutions of all participants. The average Dice similarity coefficient scores for all submissions ranged from 76.68\% to 86.70\%, and 70.42\% to 73.44\% for OARs and GTVs, respectively. We conclude that the segmentation of large-size OARs is well-addressed, and more efforts are needed for GTVs and small-size or thin-structure OARs. The benchmark will remain publicly available here: https://segrap2023.grand-challenge.org

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。