Automatic segmentation of coronary arteries is a crucial prerequisite in assisting in the diagnosis of coronary artery disease. However, due to the fuzzy boundaries, small-slender branches, and significant individual variations, automatic segmentation of coronary arteries is extremely challenging. To address these challenges, this study proposes a residual Mamba with high-order wavelet-enhanced convolution and attention feature aggregation (HWA-ResMamba). The network consists of three core modules: high-order wavelet-enhanced convolution block (HWCB), residual Mamba (ResMamba) module, and attention feature aggregation (AFA) module. Firstly, the HWCB captures low-frequency information of the image in the shallow layers of the network, allowing for detailed exploration of subtle changes in the boundaries of coronary arteries. Secondly, the ResMamba module establishes long-range dependencies between features in the deep layers of the encoder and at the beginning of the decoder, improving the continuity of the segmentation process. Finally, the
AFA module in the decoder reduces semantic differences between the encoder and decoder, which can capture small-slender coronary artery branches and further improve segmentation accuracy. Experiments on two coronary artery segmentation datasets have shown that the
HWA-ResMamba outperforms other state-of-the-art methods in terms of performance and generalization. Specifically, in the self-built dataset, HWA-ResMamba obtained Dice of
0.8857 and Hausdorff Distance (HD) of 1.9028, outperforming nnUnet by 0.0521, and 0.5489, respectively. HWA-ResMamba obtained Dice of 0.8371, and HD of 3.7205 in the public dataset, outperforming nnUnet by 0.0255, and 2.7533, respectively. These results demonstrate that the proposed model performs well in segmenting coronary arteries.
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