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

翻译：放射治疗是鼻咽癌（NasoPharyngeal Carcinoma, NPC）的一种主要且有效的治疗策略。在放射治疗中，精准勾画大体肿瘤体积（Gross Tumor Volumes, GTVs）和危及器官（Organs-At-Risk, OARs）至关重要，直接影响患者预后。以往，GTVs和OARs的勾画由经验丰富的放射肿瘤科医生完成。近年来，深度学习在许多医学图像分割任务中取得了显著成果。然而，针对NPC的OARs和GTVs分割，目前可用于模型开发与评估的公开数据集较少。为缓解这一问题，SegRap2023挑战赛与MICCAI2023联合举办，并提供一个大规模基准测试集，包含来自200名NPC患者的400次计算机断层扫描（Computed Tomography, CT）图像，每位患者均有一对预配准的非增强和增强CT扫描。该挑战赛的目标是从这对CT扫描中分割出45个OARs和2个GTVs。本文详细介绍了该挑战赛，并分析了所有参赛者的解决方案。所有提交结果的平均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的分割仍需更多努力。该基准测试将持续公开，访问地址：https://segrap2023.grand-challenge.org