  Coronary artery disease (CAD) is one of the most prevalent diseases in the
cardiovascular field and one of the major contributors to death worldwide.
Computed Tomography Angiography (CTA) images are regarded as the authoritative
standard for the diagnosis of coronary artery disease, and by performing vessel
segmentation and stenosis detection on CTA images, physicians are able to
diagnose coronary artery disease more accurately. In order to combine the
advantages of both the base model and the domain-specific model, and to achieve
high-precision and fully-automatic segmentation and detection with a limited
number of training samples, we propose a novel architecture, SAM-VMNet, which
combines the powerful feature extraction capability of MedSAM with the
advantage of the linear complexity of the visual state-space model of VM-UNet,
giving it faster inferences than Vision Transformer with faster inference speed
and stronger data processing capability, achieving higher segmentation accuracy
and stability for CTA images. Experimental results show that the SAM-VMNet
architecture performs excellently in the CTA image segmentation task, with a
segmentation accuracy of up to 98.32% and a sensitivity of up to 99.33%, which
is significantly better than other existing models and has stronger domain
adaptability. Comprehensive evaluation of the CTA image segmentation task shows
that SAM-VMNet accurately extracts the vascular trunks and capillaries,
demonstrating its great potential and wide range of application scenarios for
the vascular segmentation task, and also laying a solid foundation for further
stenosis detection.
