arXiv 2502.143632025-02-20eess.IVcs.CV

用于术后复发性鼻咽癌CT扫描中气道结构分割的拓扑感知Wavelet Mamba

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

School of Software Engineering, Sun Yat-sen University（中山大学软件工程学院）；School of Microelectronics, Shanghai University（上海大学微电子学院）；Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港科技大学电子与计算机工程系）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Eye & ENT Hospital, Fudan University（复旦大学附属眼耳鼻喉科医院）

Haishan Huang, Pengchen Liang, Naier Lin, Luxi Wang, Bin Pu, Jianguo Chen, Qing Chang, Xia Shen, Guo Ran

备注：: 20 pages, 11 figures, 6 tables

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AI总结：

提出TopoWMamba分割模型，融合小波多尺度特征、状态空间建模与拓扑感知模块，在NPCSegCT和SegRap 2023上提升术后复发性鼻咽癌CT气道结构分割性能。

摘要：

鼻咽癌（NPC）患者常接受放化疗，这可能导致张口受限和关节僵硬等术后并发症，尤其在需要再次手术的复发病例中更是如此。这些并发症会影响气道功能，因此准确的术后气道风险评估对患者护理管理至关重要。在术后CT扫描中准确分割气道相关结构，是评估这些风险的关键。本研究提出TopoWMamba（Topology-aware Wavelet Mamba，拓扑感知小波Mamba），这是一种专门为应对复发性NPC患者术后气道风险评估挑战而设计的新型分割模型。TopoWMamba结合了基于小波的多尺度特征提取、状态空间序列建模和拓扑感知模块，以稳健地分割CT扫描中的气道相关结构。通过利用基于小波的Mamba块（WMB）进行分层频率分解，并利用Snake Conv VSS（SCVSS）模块保持解剖连续性，TopoWMamba有效捕捉细粒度边界和全局结构上下文，这对于复杂术后场景中的准确分割至关重要。在NPCSegCT数据集上的大量测试中，TopoWMamba取得88.02%的平均Dice分数，优于UNet、Attention UNet和SwinUNet等现有模型。此外，TopoWMamba还在SegRap 2023 Challenge数据集上接受测试，在气管分割方面表现出显著提升，Dice分数达到95.26%。所提出的模型为自动分割提供了坚实基础，可实现更准确的术后气道风险评估。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。