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 four other U-Net based methods used in medical applications, by achieving the highest per-lesion Dice scores across all four lesions.

翻译：Agatston评分，即四条主要冠状动脉钙化总和，已广泛用于冠状动脉疾病（CAD）的诊断。然而，许多研究强调了血管特异性Agatston评分的重要性，因为特定血管的钙化与冠心病（CHD）的发生显著相关。本文提出残差块启发式坐标注意力U-Net（RICAU-Net），该网络以两种独特方式融合坐标注意力机制，并采用定制组合损失函数进行病灶特异性冠状动脉钙化（CAC）分割。该方法旨在解决稀疏小CAC病灶伴随的高度类别不平衡问题。实验结果与消融研究表明，所提方法在全部四类病灶上均取得最高的单病灶Dice分数，优于其他四种基于U-Net的医学应用方法。