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

Xiangde Luo, Jia Fu, Yunxin Zhong, Shuolin Liu, Bing Han +37 more

12/15/2023

eess.IVcs.CV

No official code found. Best implementation: HiLab-git/SegRap2023 (30 stars, last commit 2025-02-23).

Abstract

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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Code Implementations(1)

HiLab-git/SegRap2023Community100%

[SegRap2023&MedIA2025]A challenge about organ-at-risk and gross-tumor-volume segmentation in adaptive radiotherapy hosted on MICCAI2023.

304Shell, Python1 years ago

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Cite this paper

@article{luo2023segrap,
  title  = {SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma},
  author = {Xiangde Luo and Jia Fu and Yunxin Zhong and Shuolin Liu and Bing Han and Mehdi Astaraki and Simone Bendazzoli and Iuliana Toma-Dasu and Yiwen Ye and Ziyang Chen and Yong Xia and Yanzhou Su and Jin Ye and Junjun He and Zhaohu Xing and Hongqiu Wang and Lei Zhu and Kaixiang Yang and Xin Fang and Zhiwei Wang and Chan Woong Lee and Sang Joon Park and Jaehee Chun and Constantin Ulrich and Klaus H. Maier-Hein and Nchongmaje Ndipenoch and Alina Miron and Yongmin Li and Yimeng Zhang and Yu Chen and Lu Bai and Jinlong Huang and Chengyang An and Lisheng Wang and Kaiwen Huang and Yunqi Gu and Tao Zhou and Mu Zhou and Shichuan Zhang and Wenjun Liao and Guotai Wang and Shaoting Zhang},
  year   = {2023},
  eprint = {2312.09576},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.09576v1}
}

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