arXiv 2408.074442024-08-14eess.IVcs.CV

基于拓扑引导可变形Mamba的肋软骨分割：方法与基准

Costal Cartilage Segmentation with Topology Guided Deformable Mamba: Method and Benchmark

Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院整形外科医院）；School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）；Nanjing University of Science and Technology（南京理工大学）；The Second Hospital of Hebei Medical University（河北医科大学第二医院）

Senmao Wang, Haifan Gong, Runmeng Cui, Boyao Wan, Yicheng Liu, Zhonglin Hu, Haiqing Yang, Jingyang Zhou, Bo Pan, Lin Lin, Haiyue Jiang

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AI总结：

针对肋软骨解剖结构复杂导致的精准分割难题，提出融合拓扑先验的拓扑引导可变形Mamba方法，构建含165例的分割基准，实验证明其在精度和鲁棒性上优于现有方法。

摘要：

肋软骨分割对多种医学应用至关重要，由于其解剖结构复杂，且准确诊断与手术规划的需求很高，因此需要精准可靠的分割技术。我们提出了一种基于深度学习的新方法，名为拓扑引导可变形Mamba（topology-guided deformable Mamba, TGDM），用于肋软骨分割。TGDM专门用于捕捉肋软骨复杂的长距离关联，该方法利用融合了拓扑先验的可变形模型，提升了分割过程的适应性与准确性。此外，我们构建了一个包含165个病例的综合性肋软骨分割基准，该基准为评估肋软骨分割技术设立了新的标准，也为未来研究提供了宝贵资源。在域内基准和域外测试集上开展的大量实验表明，我们的方法优于现有方法，在分割精度和鲁棒性上均有显著提升。

英文摘要：

Costal cartilage segmentation is crucial to various medical applications, necessitating precise and reliable techniques due to its complex anatomy and the importance of accurate diagnosis and surgical planning. We propose a novel deep learning-based approach called topology-guided deformable Mamba (TGDM) for costal cartilage segmentation. The TGDM is tailored to capture the intricate long-range costal cartilage relationships. Our method leverages a deformable model that integrates topological priors to enhance the adaptability and accuracy of the segmentation process. Furthermore, we developed a comprehensive benchmark that contains 165 cases for costal cartilage segmentation. This benchmark sets a new standard for evaluating costal cartilage segmentation techniques and provides a valuable resource for future research. Extensive experiments conducted on both in-domain benchmarks and out-of domain test sets demonstrate the superiority of our approach over existing methods, showing significant improvements in segmentation precision and robustness.

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。