构建多中心多模态肿瘤分割基准，提升放疗规划自动化水平
SegRap2025: A Benchmark of Gross Tumor Volume and Lymph Node Clinical Target Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
- 设计跨中心、跨模态的鼻咽癌靶区分割挑战赛
- 最佳模型在外部测试集上肿瘤分割DSC达56.79%
- 为临床可用的放疗自动化系统提供评估标准
准确从计算机断层扫描（CT）中勾画出大体肿瘤体积（GTV）、淋巴结临床靶区（LN CTV）及危及器官（OAR），对鼻咽癌精准放疗计划至关重要。基于2023年仅聚焦于单中心配对非增强CT（ncCT）与增强CT（ceCT）的分割任务，SegRap2025挑战赛旨在提升分割模型在不同影像中心和模态间的泛化能力。本挑战包含两项任务：任务01使用来自SegRap2023数据集的配对CT进行GTV分割，并引入外部测试集以评估跨中心泛化性；任务02则基于多中心训练数据和未见外部测试集，处理配对或单一模态（ceCT或ncCT）CT图像，强调跨中心与跨模态鲁棒性。本文介绍挑战设置并分析十支参赛团队提交方案。在GTV分割任务中，最优模型在内部和外部测试集上的平均骰子相似系数（DSC）分别为74.61%和56.79%；在LN CTV分割任务中，最高平均DSC分别达到60.24%（配对CT）、60.50%（ceCT仅）、57.23%（ncCT仅）。SegRap2025建立了一个大规模多中心、多模态的分割基准，为评估放疗靶区分割的泛化性与鲁棒性提供重要支撑。基准数据可访问：https://hilab-git.github.io/SegRap2025_Challenge。
原文摘要 · Abstract (English)
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.
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