# Unleashing Vision Foundation Models for Coronary Artery Segmentation: Parallel ViT-CNN Encoding and Variational Fusion Source: [Unleashing Vision Foundation Models for Coronary Artery Segmentation: Parallel ViT-CNN Encoding and Variational Fusion]() Collection index: [llms.txt]() Data updated: 2026-01-02T10:45:40.606Z Authors: Caixia Dong, Duwei Dai, Xinyi Han, Fan Liu, Xu Yang, Zongfang Li, Songhua Xu Published: 2025-07-17T09:25:00.000Z ## Abstract Accurate coronary artery segmentation is critical for computeraided diagnosis of coronary artery disease (CAD), yet it remains challenging due to the small size, complex morphology, and low contrast with surrounding tissues. To address these challenges, we propose a novel segmentation framework that leverages the power of vision foundation models (VFMs) through a parallel encoding architecture. Specifically, a vision transformer (ViT) encoder within the VFM captures global structural features, enhanced by the activation of the final two ViT blocks and the integration of an attention-guided enhancement (AGE) module, while a convolutional neural network (CNN) encoder extracts local details. These complementary features are adaptively fused using a cross-branch variational fusion (CVF) module, which models latent distributions and applies variational attention to assign modality-specific weights. Additionally, we introduce an evidential-learning uncertainty refinement (EUR) module, which quantifies uncertainty using evidence theory and refines uncertain regions by incorporating multi-scale feature aggregation and attention mechanisms, further enhancing segmentation accuracy. Extensive evaluations on one in-house and two public datasets demonstrate that the proposed framework significantly outperforms state-of-the-art methods, achieving superior performance in accurate coronary artery segmentation and showcasing strong generalization across multiple datasets. The code is available at https://github.com/d1c2x3/CAseg. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Coronary Artery Segmentation on CCTA119](): Coronary artery segmentation; CCTA119; DSC: 87.31. - [Coronary Artery Segmentation on ASOCA](): Coronary artery segmentation; ASOCA; DSC: 90.15. - [Coronary Artery Segmentation on ICAS-100](): Coronary artery segmentation; ICAS-100; DSC: 82.15. - [Coronary Artery Segmentation on ASOCA cross-dataset (test)](): Coronary artery segmentation; ASOCA cross-dataset (test); DSC: 85.26. - [Coronary artery segmentation on ICAS-100 cross-dataset (test)](): Coronary artery segmentation; ICAS-100 cross-dataset (test); DSC: 78.74. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()