SegRap2025: A Benchmark of Gross Tumor Volume and Lymph Node Clinical Target Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918311153893376 |
|---|---|
| author | Fu, Jia Wang, Litingyu Li, He Luo, Zihao Wang, Huamin Bian, Chenyuan Gao, Zijun Gu, Chunbin Weng, Xin Wu, Jianghao Wu, Yicheng Ye, Jin Li, Linhao Ye, Yiwen Xia, Yong Tappeiner, Elias He, Fei qayyum, Abdul Mazher, Moona Niederer, Steven A Chen, Junqiang Huang, Chuanyi Wang, Lisheng Xing, Zhaohu Wang, Hongqiu Zhu, Lei Zhang, Shichuan Zhang, Shaoting Liao, Wenjun Wang, Guotai |
| author_facet | Fu, Jia Wang, Litingyu Li, He Luo, Zihao Wang, Huamin Bian, Chenyuan Gao, Zijun Gu, Chunbin Weng, Xin Wu, Jianghao Wu, Yicheng Ye, Jin Li, Linhao Ye, Yiwen Xia, Yong Tappeiner, Elias He, Fei qayyum, Abdul Mazher, Moona Niederer, Steven A Chen, Junqiang Huang, Chuanyi Wang, Lisheng Xing, Zhaohu Wang, Hongqiu Zhu, Lei Zhang, Shichuan Zhang, Shaoting Liao, Wenjun Wang, Guotai |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20575 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SegRap2025: A Benchmark of Gross Tumor Volume and Lymph Node Clinical Target Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma Fu, Jia Wang, Litingyu Li, He Luo, Zihao Wang, Huamin Bian, Chenyuan Gao, Zijun Gu, Chunbin Weng, Xin Wu, Jianghao Wu, Yicheng Ye, Jin Li, Linhao Ye, Yiwen Xia, Yong Tappeiner, Elias He, Fei qayyum, Abdul Mazher, Moona Niederer, Steven A Chen, Junqiang Huang, Chuanyi Wang, Lisheng Xing, Zhaohu Wang, Hongqiu Zhu, Lei Zhang, Shichuan Zhang, Shaoting Liao, Wenjun Wang, Guotai Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | SegRap2025: A Benchmark of Gross Tumor Volume and Lymph Node Clinical Target Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.20575 |