Automatic myocardial infarction detection in contrast echocardiography based on polar residual network
Automatic myocardial infarction detection in contrast echocardiography based on polar residual network
复制标题
基于极残差网络的超声心动图自动检测心肌梗死
DOI：
10.1016/j.cmpb.2020.105791
复制
发表时间：
2021-01-01
影响因子：
6.1
通讯作者：
Siuly, Siuly
中科院分区：
文献类型：
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作者：
Guo, Yanhui;Du, Guo-Qing;Siuly, Siuly
目的：心脏病是导致死亡的主要原因之一。在心血管疾病患者中，心肌梗死（MI）是主要原因。准确及时地识别MI对早期治疗具有重要意义。心肌声学造影（MCE）是临床上广泛应用于心肌梗死（MI）诊断的一种方法。然而，使用MCE的现有临床检查是主观的，并且高度依赖于操作者并且耗时。因此，在MCE中自动计算机辅助MI检测是必要的，以提高诊断性能，减少临床医生的工作量。研究方法：在这项研究中，提出了一种新的深度学习模型，极性残差网络（PResNet），以识别MCE图像中的MI区域，该图像设计了一个考虑心肌环形形状的极性层。MCE图像被输入到PResNet中，新定义的极层用于描述具有环形形状的心肌。该方法将整个极坐标图像均匀划分为若干个子区域，并改进残差网络将子区域分为正常和异常两类。最后，将检测结果映射回原始图像，以说明梗死区域的位置，以便进一步处理。结果如下：为了评估所提出的PResNet，通过对五只小鼠进行MCE来构建数据集，这些小鼠经历了左前降支动脉结扎并接受促红细胞生成素或生理盐水注射，并且面积变化分数由经验丰富的专家手动注释为金标准。结果表明，所提出的PResNet模型在两个不同的测试集上分别实现了99.6%和98.7%以及0.999和0.996的AUC（受试者操作曲线下面积）值的高分类精度。结果表明，所提出的模型可以使准确的梗死检测和诊断的MCE图像。结论：这些效率提高突出了使用极层和残差网络描述和解释MCE图像的强大能力。PResNet可以帮助临床医生在MCE上快速准确地评估梗死心肌。(c)2020爱思唯尔B.V.保留所有权利。
Purpose: Heart disease is one of the leading causes of death. Among patients with cardiovascular diseases, myocardial infarction (MI) is the main reason. Precise and timely identification of MI is significant for early treatment. Myocardial contrast echocardiography (MCE) is widely used for the detection of MI in clinic practice. However, existing clinical exam using MCE is subjective and highly operator dependent and time-consuming. Hence an automatic computer-aided MI detection in MCE is necessary to improve the diagnosis performance and decrease the workload of clinicians. Methods: In this study, a novel deep learning model, polar residual network (PResNet) is proposed to identify MI regions in MCE images which design a polar layer considering the ring shape of the myocardium. MCE images are fed into the PResNet and a newly defined polar layer is used to describe the myocardium with a ring shape. The whole polar images are evenly divided into several subsections and a residual network is improved to classify the subsection into normal and abnormal categories. Finally, the detection results are mapped back to the original image to illustrate the infarction regions' locations for the further process. Results: To evaluate the proposed PResNet, a dataset is constructed via performing MCE on five mice, which underwent the left anterior descending artery ligation and receive erythropoietin or saline injection, and the area variation fraction is manually annotated by an experienced expert as golden standards. The results demonstrate that the proposed PResNet model accomplishes high classification precisions with 99.6% and 98.7%, and 0.999 and 0.996 of AUC (area under the receiver operator curve) values on two different testing sets, respectively. Results suggest that the proposed model could enable accurate infarct detection and diagnosis of the MCE images. Conclusion: Those efficiency gains highlight the powerful ability to describe and interpret the MCE images using the polar layer and residual network. The proposed PResNet might aid the clinicians in fast and accurate assessing the infarcted myocardium on MCE. (c) 2020 Elsevier B.V. All rights reserved.