Costal Cartilage Segmentation with Topology Guided Deformable Mamba: Method and Benchmark
<|reference_start|>Costal Cartilage Segmentation with Topology Guided Deformable Mamba: Method and Benchmark: 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.<|reference_end|>
@article{wang2024costal,
    title={Costal Cartilage Segmentation with Topology Guided Deformable Mamba:
  Method and Benchmark},
    author={Senmao Wang, Haifan Gong, Runmeng Cui, Boyao Wan, Yicheng Liu,
  Zhonglin Hu, Haiqing Yang, Jingyang Zhou, Bo Pan, Lin Lin, Haiyue Jiang},
    journal={arXiv preprint arXiv:2408.07444},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07444},
    primaryClass={eess.IV cs.CV}
}