关键词: 关键词: 冠状动脉钙化斑块分割, 计算机断层扫描血管造影, 多尺度上下文, 注意力机制, 解剖先验

Abstract: Abstract: To address the problem that coronary artery calcified plaques in computed tomography angiography (CTA) images are small, sparsely distributed, and are easily confused with high-density structures such as contrast-filled lumens, bones, and stents, which leads to missed detections and false positives, a coronary artery calcified plaque segmentation method based on anatomical priors and Multi?Scale Context Refinement Network (MSCR?Net) was proposed in this paper. This method adopts a two-stage cascaded segmentation framework. First, a coronary lumen probability map was generated by our previously developed nnU-Net v2-based lumen segmentation network, serving as the anatomical spatial prior for the second stage. Second, MSCR?Net was constructed as a dual-input network, fed with raw CTA volumes and the precomputed coronary prior map, where the prior constrained the search space for calcifications. In the shallow layers, a local multi?scale residual encoder was adopted to extract fine-grained textures and local context; in the bottleneck, a customized Atrous Spatial Pyramid Pooling (ASPP) combined with a Convolutional Block Attention Module (CBAM) was employed to aggregate multi-scale semantic information and suppress high-density background interference; and in the decoder, a progressive refinement strategy was used to gradually recover boundary details. Finally, a spatially weighted joint loss integrating Tversky loss and Focal loss was introduced, together with deep supervision and prior-guided post-processing, to alleviate severe foreground-background imbalance and improve the learning of hard samples. Experimental results on 139 high-resolution CTA scans demonstrate that the proposed method achieves a Dice Similarity Coefficient (DSC) of 75.99%, outperforming nnU-Net by 4.33 percentage points, and reduces the 95% Hausdorff Distance (HD95) to 16.55 mm, which is 6.32 mm lower than that of Residual U-Net, with only 15.73 M parameters, while maintaining relatively low computational cost and achieving superior segmentation accuracy and boundary localization performance. The proposed method can provide a reliable imaging reference for automated quantitative analysis of coronary calcification and facilitate computer-aided diagnosis and risk assessment of cardiovascular diseases (CVD).

Key words: Keywords: coronary artery calcified plaque segmentation, Computed Tomography Angiography (CTA), multi-scale context, attention mechanism, coronary anatomical prior