VesMamba: Vessel Morphology-Enhanced State Space Model for Cerebrovascular Delineation

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Neuroscience

Background:

• Accurate delineation of cerebrovascular structures is crucial for diagnosing and treating cerebrovascular diseases.
• Current deep learning methods face challenges in capturing complex vascular topology and fine details from TOF-MRA and CTA.

Purpose of the Study:

• To develop an advanced deep learning framework, VesMamba, for improved cerebrovascular structure segmentation.
• To integrate explicit vascular morphological priors into a state-space model for enhanced accuracy.

Main Methods:

• Proposed VesMamba framework integrating explicit vascular morphological priors into a state-space model.
• Introduced Tri-oriented Vessel-aware Mamba (ToVM) module for dynamic sequence reordering based on vascular edge features.
• Implemented a 3D Large-Small Gated Convolution (LSGC) module to preserve spatial information.

Main Results:

• VesMamba demonstrated superior performance across most evaluation metrics compared to eight state-of-the-art methods.
• Extensive experiments were conducted on two TOF-MRA and one CTA dataset.
• The proposed ToVM and LSGC modules effectively addressed limitations of generic SSM-based methods.

Conclusions:

• VesMamba offers a significant advancement in deep learning for cerebrovascular segmentation.
• The framework's ability to model complex vascular structures surpasses existing approaches.
• VesMamba shows promise for clinical applications in cerebrovascular disease diagnosis and treatment.