Under the background of high incidence and mortality of cardiovascular diseases, the accurate and automatic left ventricle (LV) segmentation method is of essential importance for the diagnosis of the cardiovascular system. However, fully automatic LV segmentation remains challenging due to the complex structure of cardiac magnetic resonance image (MRI) and the morphological changes of LV caused by various cardiovascular diseases. In this paper, we propose a novel parallel end-to-end convolutional neural network (CNN) for LV segmentation. Our network consists of two interactive subnetworks which utilize essentially identical but formally different labels in the hope that they can learn segmentation from different perspectives. The two subnetworks take the same cardiac MRI as input and output a pair of segmentation maps in different forms. After averaging the two segmentation maps obtained from the two subnetworks, we get the final contours of the endocardium (endo) and epicardium (epi) simultaneously. The proposed method is evaluated on the dataset provided by the Left Ventricle Full Quantification Challenge of MICCAI 2019. The average Dice scores on epi, endo, and myocardium (myo) reach 0.961, 0.949, and 0.867 respectively which outperform the other methods. The experimental results show that our method has the potential for clinical application.

在心血管疾病的高发病率和高死亡率的背景下，准确而自动的左心室（LV）分割方法对于心血管系统的诊断至关重要。然而，由于心脏磁共振图像（MRI）的复杂结构以及各种心血管疾病引起的左室形态变化，全自动左室分割仍然具有挑战性。在本文中，我们提出了一种用于LV分割的新型并行端到端卷积神经网络（CNN）。我们的网络由两个交互式子网组成，它们利用本质上相同但形式上不同的标签，希望它们可以从不同的角度学习分段。这两个子网采用相同的心脏MRI作为输入，并以不同形式输出一对分割图。在对从两个子网获得的两个分割图进行平均后，我们可以同时获得心内膜（endo）和心外膜（epi）的最终轮廓。该方法在MICCAI 2019左心室完全量化挑战赛提供的数据集上进行了评估。epi，内膜和心肌（myo）的平均Dice得分分别达到0.961、0.949和0.867，优于其他方法。实验结果表明我们的方法具有临床应用潜力。和心肌（myo）分别达到0.961、0.949和0.867，优于其他方法。实验结果表明，该方法具有临床应用潜力。和心肌（myo）分别达到0.961、0.949和0.867，优于其他方法。实验结果表明我们的方法具有临床应用潜力。