Boundary-Aware Vision Transformer for Angiography Vascular Network Segmentation
This work addresses the problem of precise vascular segmentation in medical imaging for clinical applications, representing an incremental improvement over existing methods.
The paper tackled the challenge of accurately segmenting vascular structures in coronary angiography by introducing BAVT, a Boundary-Aware Vision Transformer enhanced with an edge-aware loss, which achieved superior performance on the DCA-1 dataset, outperforming CNN and hybrid baselines.
Accurate segmentation of vascular structures in coronary angiography remains a core challenge in medical image analysis due to the complexity of elongated, thin, and low-contrast vessels. Classical convolutional neural networks (CNNs) often fail to preserve topological continuity, while recent Vision Transformer (ViT)-based models, although strong in global context modeling, lack precise boundary awareness. In this work, we introduce BAVT, a Boundary-Aware Vision Transformer, a ViT-based architecture enhanced with an edge-aware loss that explicitly guides the segmentation toward fine-grained vascular boundaries. Unlike hybrid transformer-CNN models, BAVT retains a minimal, scalable structure that is fully compatible with large-scale vision foundation model (VFM) pretraining. We validate our approach on the DCA-1 coronary angiography dataset, where BAVT achieves superior performance across medical image segmentation metrics outperforming both CNN and hybrid baselines. These results demonstrate the effectiveness of combining plain ViT encoders with boundary-aware supervision for clinical-grade vascular segmentation.