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

Authors

Lei Xie, Jiangxu Zhang, Jiawei Zhang, Qingrun Zeng, Yuanjing Feng

Published in

NMR in biomedicine. Volume 39. Issue 9. Pages e70339.

Abstract

Accurate delineation of cerebrovascular structures from Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) and Computed Tomography Angiography (CTA) is essential for the clinical diagnosis and treatment of cerebrovascular diseases. However, the intricate topology and fine-scale nature of cerebral vessels pose significant challenges for deep learning methods, which often struggle to capture long-range dependencies and precise morphological details. In this work, we propose VesMamba, a deep learning framework that integrates explicit vascular morphological priors into a state-space model. Unlike generic SSM-based methods that rely on fixed scanning patterns, we introduce a Tri-oriented Vessel-aware Mamba (ToVM) module, which dynamically reorders input 1D sequences using cerebrovascular edge features to better model complex vascular structures. Complementarily, we present the 3D Large-Small Gated Convolution (LSGC) module after the ToVM module to preserve critical spatial information. We conducted extensive experiments on two TOF-MRA and one CTA dataset, comparing our method with eight state-of-the-art approaches. Our results show that VesMamba achieves superior performance on the majority of evaluation metrics relative to all competing methods.

PMID:
42469936
Bibliographic data and abstract were imported from PubMed on 18 Jul 2026.

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