针对心脏CT中小而稀疏的钙化病灶，提出新型分割网络提升诊断精度。
RICAU-Net: Residual-block Inspired Coordinate Attention U-Net for Segmentation of Small and Sparse Calcium Lesions in Cardiac CT
- 借鉴残差块设计坐标注意力机制，增强小病灶特征捕捉能力
- 在四支冠脉病灶上均取得最高病灶级Dice分数
- 特别适合临床需要精准定位特定血管钙化的场景
阿加斯顿评分是评估冠心病（CAD）的常用指标，但近年来研究强调各冠状动脉特异性阿加斯顿评分的重要性，因为特定血管钙化与冠心病发生显著相关。本文提出一种基于残差块启发的坐标注意力U-Net（RICAU-Net），通过两种方式引入坐标注意力机制，并设计定制化组合损失函数，用于病变特异性冠状动脉钙化（CAC）分割。该方法旨在解决小而稀疏钙化病灶带来的严重类别不平衡问题。实验结果及消融研究显示，所提方法在所有四支冠脉病灶上的病灶级Dice分数均优于五种其他基于U-Net的医学图像分割方法。
原文摘要 · Abstract (English)
The Agatston score, which is the sum of the calcification in the four main coronary arteries, has been widely used in the diagnosis of coronary artery disease (CAD). However, many studies have emphasized the importance of the vessel-specific Agatston score, as calcification in a specific vessel is significantly correlated with the occurrence of coronary heart disease (CHD). In this paper, we propose the Residual-block Inspired Coordinate Attention U-Net (RICAU-Net), which incorporates coordinate attention in two distinct manners and a customized combo loss function for lesion-specific coronary artery calcium (CAC) segmentation. This approach aims to tackle the high class-imbalance issue associated with small and sparse CAC lesions. Experimental results and the ablation study demonstrate that the proposed method outperforms the five other U-Net based methods used in medical applications, by achieving the highest per-lesion Dice scores across all four lesions.
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