Background and Objective: Automatic cardiac segmentation plays an utmost role in the diagnosis and quantification of cardiovascular diseases. Methods: This paper proposes a new cardiac segmentation method in short-axis Magnetic Resonance Imaging (MRI) images, called attention U-Net architecture with input image pyramid and deep supervised output layers (AID), which can fully-automatically learn to pay attention to target structures of various sizes and shapes. During each training process, the model continues to learn how to emphasize the desired features and suppress irrelevant areas in the original images, effectively improving the accuracy of cardiac segmentation. At the same time, we introduce the Focal Tversky Loss (FTL), which can effectively solve the problem of high imbalance in the amount of data between the target class and the background class during cardiac image segmentation. In order to obtain a better representation of intermediate features, we add a multi-scale input pyramid to the attention network. Results: The proposed cardiac segmentation technique is tested on the public Left Ventricle Segmentation Challenge (LVSC) dataset, which is shown to achieve 0.75, 0.87 and 0.92 for Jaccard Index, Sensitivity and Specificity, respectively. Experimental results demonstrate that the proposed method is able to improve the segmentation accuracy compared with the standard U-Net, and achieves comparable performance to the most advanced fully-automated methods. Conclusions: Given its effectiveness and advantages, the proposed method can facilitate cardiac segmentation in short-axis MRI images in clinical practice.

背景与目的：自动心脏分割在心血管疾病的诊断和量化中起着至关重要的作用。方法：本文提出了一种在短轴磁共振成像（MRI）图像中进行心脏分割的新方法，即具有输入图像金字塔和深监督输出层（AID）的注意力U-Net架构，该方法可以全自动学习注意目标各种大小和形状的结构。在每个训练过程中，模型都会继续学习如何强调所需的特征并抑制原始图像中不相关的区域，从而有效提高心脏分割的准确性。同时，我们引入了焦点Tversky损失（FTL），可以有效解决心脏图像分割过程中目标类别和背景类别之间的数据量高度不平衡的问题。为了更好地表示中间特征，结果：在公开的左心室分割挑战赛（LVSC）数据集上对提出的心脏分割技术进行了测试，结果显示Jaccard指数，敏感性和特异性分别达到0.75、0.87和0.92。实验结果表明，与标准U-Net相比，该方法能够提高分割精度，并且可以达到与最先进的全自动方法相当的性能。结论：鉴于其有效性和优势，该方法可在临床实践中促进短轴MRI图像中的心脏分割。