VesMamba: 3D Pulmonary Vessel Segmentation from CT images via Mamba with Structural Perception and Scale-aware Filtering

Automated 3D pulmonary vessel segmentation from CT images is crucial for improving early screening and assessment of pulmonary vessel related diseases. However, it remains an extremely challenging task due to the complex and tree-like structures of vessels, large scale-variation, and the existence of highly similar tissues in the background. Existing segmentation models either cannot sufficiently capture long-range structural dependencies, which are of great importance in vessel segmentation, or are constrained by insufficient computational resources in clinical settings. In this paper, we propose VesMamba, a novel model for 3D pulmonary vessel segmentation that comprehensively addresses these challenges. Specifically, we first devise a spatial-gated structural perception (SSP) module, which employs Mamba to efficiently capture long-range dependencies. In SSP, we design dynamic spatial attention convolutions (DSAC) for dynamically learning the tree-like 3D vessel structures, providing Mamba with the spatial perception capability to better track the complicated topologies of vessels. Second, we propose an innovative bidirectional scale-aware filter (BSF) module to strengthen the representation capability of the encoder, facilitating our model to focus on vessels of different scales under noise. Moreover, we apply a mask-constrained decoder to further improve segmentation consistency and accuracy, which constrains the inference of adjacent low-layer decoders directly by high-layer masks. Extensive experiments on the public dataset Parse22 and the internal dataset Lung79 demonstrate that our method can achieve better performance than SOTAs. Codes will be released upon publication.