Myocardial Region-guided Feature Aggregation Net for Automatic Coronary artery Segmentation and Stenosis Assessment using Coronary Computed Tomography Angiography

Coronary artery disease (CAD) remains a leading cause of mortality worldwide, requiring accurate segmentation and stenosis detection using Coronary Computed Tomography angiography (CCTA). Existing methods struggle with challenges such as low contrast, morphological variability and small vessel segmentation. To address these limitations, we propose the Myocardial Region-guided Feature Aggregation Net, a novel U-shaped dual-encoder architecture that integrates anatomical prior knowledge to enhance robustness in coronary artery segmentation. Our framework incorporates three key innovations: (1) a Myocardial Region-guided Module that directs attention to coronary regions via myocardial contour expansion and multi-scale feature fusion, (2) a Residual Feature Extraction Encoding Module that combines parallel spatial channel attention with residual blocks to enhance local-global feature discrimination, and (3) a Multi-scale Feature Fusion Module for adaptive aggregation of hierarchical vascular features. Additionally, Monte Carlo dropout f quantifies prediction uncertainty, supporting clinical interpretability. For stenosis detection, a morphology-based centerline extraction algorithm separates the vascular tree into anatomical branches, enabling cross-sectional area quantification and stenosis grading. The superiority of MGFA-Net was demonstrated by achieving an Dice score of 85.04%, an accuracy of 84.24%, an HD95 of 6.1294 mm, and an improvement of 5.46% in true positive rate for stenosis detection compared to3D U-Net. The integrated segmentation-to-stenosis pipeline provides automated, clinically interpretable CAD assessment, bridging deep learning with anatomical prior knowledge for precision medicine. Our code is publicly available at http://github.com/chenzhao2023/MGFA_CCTA

Learning to Write Stylized Chinese Characters by Reading a Handful of Examples

Automatically writing stylized Chinese characters is an attractive yet challenging task due to its wide applicabilities. In this paper, we propose a novel framework named Style-Aware Variational Auto-Encoder (SA-VAE) to flexibly generate Chinese characters. Specifically, we propose to capture the different characteristics of a Chinese character by disentangling the latent features into content-related and style-related components. Considering of the complex shapes and structures, we incorporate the structure information as prior knowledge into our framework to guide the generation. Our framework shows a powerful one-shot/low-shot generalization ability by inferring the style component given a character with unseen style. To the best of our knowledge, this is the first attempt to learn to write new-style Chinese characters by observing only one or a few examples. Extensive experiments demonstrate its effectiveness in generating different stylized Chinese characters by fusing the feature vectors corresponding to different contents and styles, which is of significant importance in real-world applications.