Key Laboratory for Biomedical Engineering of Ministry of Education, Institute of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China.
Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou 310009, China.
Math Biosci Eng. 2023 Jan;20(2):2081-2093. doi: 10.3934/mbe.2023096. Epub 2022 Nov 14.
Myocardial contrast echocardiography (MCE) has been proposed as a method to assess myocardial perfusion for the detection of coronary artery diseases in a non-invasive way. As a critical step of automatic MCE perfusion quantification, myocardium segmentation from the MCE frames faces many challenges due to the low image quality and complex myocardial structure. In this paper, a deep learning semantic segmentation method is proposed based on a modified DeepLabV3+ structure with an atrous convolution and atrous spatial pyramid pooling module. The model was trained separately on three chamber views (apical two-chamber view, apical three-chamber view, and apical four-chamber view) on 100 patients' MCE sequences, divided by a proportion of 7:3 into training and testing datasets. The results evaluated by using the dice coefficient (0.84, 0.84, and 0.86 for three chamber views respectively) and Intersection over Union(0.74, 0.72 and 0.75 for three chamber views respectively) demonstrated the better performance of the proposed method compared to other state-of-the-art methods, including the original DeepLabV3+, PSPnet, and U-net. In addition, we conducted a trade-off comparison between model performance and complexity in different depths of the backbone convolution network, which illustrated model application feasibility.
心肌对比超声心动图（MCE）已被提议作为一种非侵入性方法来评估心肌灌注以检测冠状动脉疾病。作为自动 MCE 灌注定量的关键步骤，由于图像质量低和复杂的心肌结构，从 MCE 帧中分割心肌面临许多挑战。在本文中，提出了一种基于改进的 DeepLabV3+结构的深度学习语义分割方法，该结构具有空洞卷积和空洞空间金字塔池化模块。该模型分别在 100 名患者的 MCE 序列的三个腔室视图（心尖两腔视图、心尖三腔视图和心尖四腔视图）上进行训练，通过比例为 7:3 分为训练和测试数据集。使用骰子系数（三个腔室视图分别为 0.84、0.84 和 0.86）和交并比（三个腔室视图分别为 0.74、0.72 和 0.75）评估的结果表明，与其他最先进的方法（包括原始的 DeepLabV3+、PSPnet 和 U-net）相比，该方法的性能更好。此外，我们还在骨干卷积网络的不同深度之间进行了模型性能和复杂度之间的权衡比较，这说明了模型应用的可行性。
