nnU-Net v2 for Head-and-Neck PET/CT Primary Tumor and Lymph Node Segmentation: A Simple, Strong Baseline

Yansong Bu, Zihao Wang, Yuwan Wang, Jiexin Jiang, Yuhan Chen, Mengye Lyu

Keywords: Medical image segmentation, nnU-Net, tumor segmentation, MRI-guided radiation therapy

Abstract: Accurate segmentation of primary tumors (GTVp) and nodal lesions (GTVn) is essential for radiotherapy planning in head and neck cancer (HNC). The HECKTOR 2025 challenge provides a large-scale multi-center PET/CT dataset for benchmarking automated tumor segmentation. In this study, we developed a segmentation pipeline based on the nnU-Net v2 framework without architectural modifications. The model was trained using 5-fold cross-validation on the complete training cohort and deployed in a Docker container for submission. On the official validation leaderboard, our approach achieved a GTVp Dice of 0.7444, a GTVn Dice of 0.7956, and a GTVn F1-score of 0.5868, securing seventh place overall in Task 1. These results demonstrate that nnU-Net v2 remains a competitive baseline for multi-center PET/CT segmentation tasks, providing robust tumor delineation performance across heterogeneous datasets. We ranked third overall in the HECKTOR 2025 Challenge (Task 1).

Submission Number: 2