MambaGLA-NPC: a 3D framework integrating CNN and state space models for robust multi-center nasopharyngeal carcinoma segmentation

Background and Objective Accurate 3D segmentation of nasopharyngeal carcinoma (NPC) from MRI is critical for radiotherapy planning but remains challenging due to poor multi-center generalization, lack of spatial continuity, and high computational costs of global modeling. We aim to address these limitations by proposing a novel 3D framework. Methods We propose MambaGLA-NPC, a 3D deep learning framework integrating State Space Models (SSMs) and Convolutional Neural Networks (CNNs) in a U-shaped encoder-decoder architecture. A Conv-GLSSM module jointly extracts static local features via convolutions and global context via SSMs with a 3D local scanning strategy. A gated channel-spatial attention (GCS Att) module suppresses irrelevant noise in skip connections, and an NPC-specific adapter enables parameter-efficient fine-tuning across multi-center datasets. Results On a multi-center dataset of 1,098 NPC patients from three hospitals, our model achieves state-of-the-art performance on the internal test set (Dice: 80.47%, IoU: 67.83%, HD95: 4.09 mm). Under cross-center evaluation, it significantly outperforms prior methods (e.g., Dice of 87.13% and 76.70% on two external centers). The NPC-Adapter boosts cross-center Dice by up to 8.84% compared to no adaptation. Ablation studies confirm the contribution of each module, and the Conv-GLSSM reduces computational cost versus Transformers (21.66 GFLOPs vs. OOM for self-attention at 128 3 resolution). Conclusion MambaGLA-NPC is the first Mamba-based 3D NPC segmentation framework that effectively balances local-global feature integration with linear complexity, achieving superior accuracy and generalization across multi-center MRI data while maintaining high efficiency.