arXiv 2601.205752026-01-28eess.IVcs.CV

SegRap2025：鼻咽癌放疗计划中GTV和淋巴结临床靶区体积分割的基准测试

SegRap2025: A Benchmark of Gross Tumor Volume and Lymph Node Clinical Target Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma

School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China（电子科技大学机械与电子工程学院）；Department of Radiation Oncology, Sichuan Cancer Center, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, University of Electronic Science and Technology of China（四川省肿瘤医院放射肿瘤科）；Shanghai AI Lab, Shanghai, China（上海人工智能实验室）；The Affiliated Hospital of Qingdao University, Qingdao, China（青岛大学附属医院）；Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China（香港中文大学计算机科学与工程系）；Faculty of Information Technology, Monash University, Melbourne, Australia（墨尔本大学信息科技学院）；Department of Computing and Department of Brain Sciences, Imperial College London, United Kingdom（伦敦帝国理工学院计算机系和脑科学系）；School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China（西北工业大学计算机科学与工程学院）；UMIT Tirol - Private University for Health Sciences and Health Technology, Austria（蒂罗尔大学（私人健康科学与健康技术大学））；School of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院）；National Heart and Lung Institute, Imperial College London, London, United Kingdom（伦敦帝国理工学院国家心脏和肺研究所）；Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom（伦敦大学学院霍克斯研究所）；Shanghai MediWorks Precision Instruments Co., Ltd., China（上海 MediWorks 精密仪器有限公司）；School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China（上海交通大学自动化与智能感知学院）；Department of Systems Hub, Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China（香港科技大学（广州）系统中心）

Jia Fu, Litingyu Wang, He Li, Zihao Luo, Huamin Wang, Chenyuan Bian, Zijun Gao, Chunbin Gu, Xin Weng, Jianghao Wu, Yicheng Wu, Jin Ye, Linhao Li, Yiwen Ye, Yong… 展开作者

Jia Fu, Litingyu Wang, He Li, Zihao Luo, Huamin Wang, Chenyuan Bian, Zijun Gao, Chunbin Gu, Xin Weng, Jianghao Wu, Yicheng Wu, Jin Ye, Linhao Li, Yiwen Ye, Yong Xia, Elias Tappeiner, Fei He, Abdul qayyum, Moona Mazher, Steven A Niederer, Junqiang Chen, Chuanyi Huang, Lisheng Wang, Zhaohu Xing, Hongqiu Wang, Lei Zhu, Shichuan Zhang, Shaoting Zhang, Wenjun Liao, Guotai Wang

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AI总结：

SegRap2025旨在通过多中心、多模态的基准测试提升鼻咽癌放疗计划中GTV和LN CTV分割的通用性和鲁棒性。

摘要：

准确从CT扫描中勾画出肿瘤总病灶体积（GTV）、淋巴结临床靶区体积（LN CTV）和易受损伤的器官（OAR）对于鼻咽癌（NPC）的精确放疗计划至关重要。在SegRap2023的基础上，后者专注于使用单中心配对非增强CT（ncCT）和增强CT（ceCT）扫描进行OAR和GTV分割，SegRap2025挑战旨在提高分割模型在不同影像中心和模态下的通用性和鲁棒性。SegRap2025包含两个任务：任务01使用SegRap2023数据集的配对CT进行GTV分割，并增加一个外部测试集以评估跨中心泛化能力；任务02则专注于使用多中心训练数据和未见过的外部测试集进行LN CTV分割，每个案例包含配对CT扫描或单一模态，强调跨中心和跨模态的鲁棒性。本文提出了挑战设置，并提供了十支参赛队伍提交的解决方案的全面分析。对于GTV分割任务，表现最佳的模型在内部和外部测试组中的平均Dice相似系数（DSC）分别为74.61%和56.79%。对于LN CTV分割任务，最高平均DSC值分别达到60.24%、60.50%和57.23%，分别对应配对CT、ceCT-only和ncCT-only子集。SegRap2025建立了大规模多中心、多模态的基准测试，用于评估放疗目标分割的通用性和鲁棒性，为临床可应用的自动化放疗计划系统提供了有价值的见解。该基准测试可在：https://hilab-git.github.io/SegRap2025_Challenge访问。

英文摘要：

Accurate delineation of Gross Tumor Volume (GTV), Lymph Node Clinical Target Volume (LN CTV), and Organ-at-Risk (OAR) from Computed Tomography (CT) scans is essential for precise radiotherapy planning in Nasopharyngeal Carcinoma (NPC). Building upon SegRap2023, which focused on OAR and GTV segmentation using single-center paired non-contrast CT (ncCT) and contrast-enhanced CT (ceCT) scans, the SegRap2025 challenge aims to enhance the generalizability and robustness of segmentation models across imaging centers and modalities. SegRap2025 comprises two tasks: Task01 addresses GTV segmentation using paired CT from the SegRap2023 dataset, with an additional external testing set to evaluate cross-center generalization, and Task02 focuses on LN CTV segmentation using multi-center training data and an unseen external testing set, where each case contains paired CT scans or a single modality, emphasizing both cross-center and cross-modality robustness. This paper presents the challenge setup and provides a comprehensive analysis of the solutions submitted by ten participating teams. For GTV segmentation task, the top-performing models achieved average Dice Similarity Coefficient (DSC) of 74.61% and 56.79% on the internal and external testing cohorts, respectively. For LN CTV segmentation task, the highest average DSC values reached 60.24%, 60.50%, and 57.23% on paired CT, ceCT-only, and ncCT-only subsets, respectively. SegRap2025 establishes a large-scale multi-center, multi-modality benchmark for evaluating the generalization and robustness in radiotherapy target segmentation, providing valuable insights toward clinically applicable automated radiotherapy planning systems. The benchmark is available at: https://hilab-git.github.io/SegRap2025_Challenge.

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。