SegRap: Segmentation of Gross Tumor Volume and Lymph Node Clinical Target Volume for Radiotherapy Planning of Nasopharyngeal Carcinoma Challenge 2025
Creators: name: Fu, Jia
name: Luo, Xiangde
name: Li, He
name: Wang, Huamin
name: Wang, Litingyu
name: Zhang, Shichuan
name: Liao, Wenjun
name: Wang, Guotai
name: Zhang, Shaoting
Publisher: name: Zenodo
Year: 2025
Subjects: Gross Tumor Volume
Lymph Node Clinical Target Volume
Segmentation
Radiotherapy
MICCAI 2025 challenge

description: Radiotherapy is one of the most important cancer treatments, which utilizes external beam radiation to kill cancer cells. Effective treatment planning is crucial for radiotherapy, which sets up the radiation dose distribution for tumor volumes. The goal of treatment planning is to maximize the likelihood of a cure while minimizingradiation-related toxicity. In radiotherapy planning, Gross Tumor Volume (GTV) and Clinical Target Volume (CTV) are two key volumes of interest. GTV is the position and extent of the primary tumor imaged by Computed Tomography (CT) scans, i.e., what is visible. CTV, which encompasses the GTV, describes the extent of microscopic, unimageable tumor spread. Accurately delineating the boundaries of GTV and CTV for Nasopharyngeal Carcinoma (NPC) from CT scans is a critical step in treatment planning. However, manual delineation slice-by-slice in CT scans is time-consuming and labor-intensive for radiation oncologists. Automating this process with precise delineation of GTV and CTV would significantly reduce treatment planning time, thereby improving the efficiency of radiotherapy.

We propose the “Segmentation of Gross Tumor Volume and Lymph Node Clinical Target Volume for Radiotherapy Planning of Nasopharyngeal Carcinoma Challenge 2025 (SegRap2025)”, to be held in conjunction with MICCAI 2025. This challenge will provide two datasets of CT scans from NPC patients for GTV and Lymph Node (LN) CTV  segmentation. The GTV segmentation dataset consists of CT scans from 260 patients collected across two cohorts. Each patient has paired non-contrast CT (ncCT) and contrast-enhanced CT (ceCT) scans, with pixel-level annotations for Gross Tumor Volume of nasopharynx (GTVnx) and Gross Tumor Volume of lymph node (GTVnd). The LN CTV segmentation dataset includes CT scans from 402 patients across six cohorts, with 210 patients having paired ncCT and ceCT scans, and 192 patients with either one ncCT or ceCT scan. All scans are annotated with pixel-level segmentations for LN CTV.

This challenge offers comprehensive datasets for automatic delineation of GTV and LN CTV, which provides valuable data to the research community. It is an extension of SegRap2023, which was held at MICCAI2023 for Organ-at-Risk (OAR) and GTV segmentation (387 teams registered, with 10 and 11 teams submitting their final models for OAR and GTV evaluation, respectively. The challenge talks and ceremony attracted a large group of audience to learn about the top-ranking methods). While most teams performed well for OAR segmentation, GTV segmentation remains a challenging problem. Hence, SegRap2025 will provide an extensive and comprehensive dataset with 260 patients and two GTVs from two cohorts. CT scans from one internal cohort will be used for training and validation, while scans from both the internal cohort and an external cohort will be used for testing, evaluating the generalizability of the model. Additionally, SegRap2025 will be the first challenge to focus on LN CTV segmentation with an extensive dataset of 402 patients from six cohorts. Multi- and single-modal CT scans from five cohorts will be used for training, with scans from another external cohort used for validation and testing. The challenge will address the issue of missing modalities in LN CTV delineation. Moreover, SegRap2025 will provide an unlabeled dataset of CT scans from 500 patients to aid in model training. Based on the extensive and comprehensive datasets, two sub-tasks will be held in SegRap2025:



Task01: Gross Tumor Volume (GTV) segmentation

Task02: Lymph Node Clinical Target Volume (LN CTV) segmentation


SegRap2025 is expected to continually attract much attention from the research community and advance the research on GTV and LN CTV segmentation significantly.