Shanghai Jiao Tong University (Qin Lab) / University of Texas Southwestern Medical Center
Deep channel-attention network for segmenting the full coronary vessel tree from sequential X-ray coronary angiography (XCA) frames, rather than a single static image. An encoder-decoder architecture fuses temporal-spatial feature maps across the XCA sequence via skip connections, then uses channel-attention blocks in the decoder to refine features and separate thin vessel structures from complex, noisy backgrounds; a Dice loss addresses the severe foreground/background class imbalance typical of XCA. The authors report that SVS-net outperforms prior 2D and video-based baselines on both quantitative vessel-segmentation metrics and visual validation.
Architecture
Hybrid
Encoder-decoder network with temporal-spatial feature fusion across XCA sequence frames via skip connections, and channel-attention blocks in the decoder to refine vessel features against noisy backgrounds
Framework
Keras
Added to catalog
2026-08-13
Renji Hospital XCA Sequence Dataset (SVS-net)
Count is XCA sequences; unique patient count not stated separately
Voxel/pixel-wise segmentation of the full coronary vessel tree across sequential X-ray coronary angiography frames