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

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arxiv 2312.09576 v1 pith:VUBOZ3BC submitted 2023-12-15 eess.IV cs.CV

Xiangde Luo , Jia Fu , Yunxin Zhong , Shuolin Liu , Bing Han , Mehdi Astaraki , Simone Bendazzoli , Iuliana Toma-Dasu
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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
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classification eess.IVcs.CV
keywords oarsgtvssegmentationbenchmarkchallengeradiationscanssegrap2023
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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

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Cited by 5 Pith papers

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