Topology-Aware Wavelet Mamba for Airway Structure Segmentation in Postoperative Recurrent Nasopharyngeal Carcinoma CT Scans

• URL: http://arxiv.org/abs/2502.14363v1
• Date: Thu, 20 Feb 2025 08:40:41 GMT
• Title: Topology-Aware Wavelet Mamba for Airway Structure Segmentation in Postoperative Recurrent Nasopharyngeal Carcinoma CT Scans
• Authors: Haishan Huang, Pengchen Liang, Naier Lin, Luxi Wang, Bin Pu, Jianguo Chen, Qing Chang, Xia Shen, Guo Ran,
• Abstract summary: Nasopharyngeal carcinoma (NPC) patients often undergo radiotherapy and chemotherapy, which can lead to postoperative complications.<n>This study introduces TopoWMamba, a novel segmentation model specifically designed to address the challenges of postoperative airway risk evaluation.<n>TopoWMamba combines wavelet-based multi-scale feature extraction, state-space sequence modeling, and topology-aware modules to segment airway-related structures in CT scans robustly.
• Score: 5.139044065222533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nasopharyngeal carcinoma (NPC) patients often undergo radiotherapy and chemotherapy, which can lead to postoperative complications such as limited mouth opening and joint stiffness, particularly in recurrent cases that require re-surgery. These complications can affect airway function, making accurate postoperative airway risk assessment essential for managing patient care. Accurate segmentation of airway-related structures in postoperative CT scans is crucial for assessing these risks. This study introduces TopoWMamba (Topology-aware Wavelet Mamba), a novel segmentation model specifically designed to address the challenges of postoperative airway risk evaluation in recurrent NPC patients. TopoWMamba combines wavelet-based multi-scale feature extraction, state-space sequence modeling, and topology-aware modules to segment airway-related structures in CT scans robustly. By leveraging the Wavelet-based Mamba Block (WMB) for hierarchical frequency decomposition and the Snake Conv VSS (SCVSS) module to preserve anatomical continuity, TopoWMamba effectively captures both fine-grained boundaries and global structural context, crucial for accurate segmentation in complex postoperative scenarios. Through extensive testing on the NPCSegCT dataset, TopoWMamba achieves an average Dice score of 88.02%, outperforming existing models such as UNet, Attention UNet, and SwinUNet. Additionally, TopoWMamba is tested on the SegRap 2023 Challenge dataset, where it shows a significant improvement in trachea segmentation with a Dice score of 95.26%. The proposed model provides a strong foundation for automated segmentation, enabling more accurate postoperative airway risk evaluation.

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