Costal Cartilage Segmentation with Topology Guided Deformable Mamba: Method and Benchmark
This work addresses the need for accurate segmentation in medical applications like diagnosis and surgical planning, representing an incremental advancement with a novel method for a known bottleneck.
The paper tackles costal cartilage segmentation by proposing a topology-guided deformable Mamba (TGDM) method, which improves segmentation precision and robustness, as demonstrated through extensive experiments on a new benchmark of 165 cases.
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.