RICAU-Net：用于心脏CT中小而稀疏钙化病变分割的残差块启发的坐标注意力U-Net
RICAU-Net: Residual-block Inspired Coordinate Attention U-Net for Segmentation of Small and Sparse Calcium Lesions in Cardiac CT
AI总结：
本文提出RICAU-Net，结合坐标注意力和定制损失，用于心脏CT中小而稀疏钙化病变分割，在四种病变上取得最高Dice分数。
摘要：
Agatston评分是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，因为特定血管中的钙化与冠心病（CHD）的发生显著相关。在本文中，我们提出了残差块启发的坐标注意力U-Net（RICAU-Net），它以两种不同方式整合了坐标注意力，并采用定制的组合损失函数，用于病变特异性冠状动脉钙化（CAC）分割。该方法旨在解决与小而稀疏CAC病变相关的高度类别不平衡问题。实验结果和消融研究表明，所提出的方法优于医学应用中使用的其他五种基于U-Net的方法，在所有四种病变中取得了最高的每病变Dice分数。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
