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 Mehdi Astaraki Simone Bendazzoli Iuliana Toma-Dasu 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
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