How well the heart is functioning can be quantified through measurements of
myocardial deformation via echocardiography. Clinical assessment of cardiac
function is generally focused on global indices of relative shortening,
however, territorial, and segmental strain indices have shown to be abnormal in
regions of myocardial disease, such as scar. In this work, we propose a single
framework to predict myocardial disease substrates at global, territorial, and
segmental levels using regional myocardial strain traces as input to a
convolutional neural network (CNN)-based classification algorithm. An
anatomically meaningful representation of the input data from the clinically
standard bullseye representation to a multi-channel 2D image is proposed, to
formulate the task as an image classification problem, thus enabling the use of
state-of-the-art neural network configurations. A Fully Convolutional Network
(FCN) is trained to detect and localize myocardial scar from regional left
ventricular (LV) strain patterns. Simulated regional strain data from a
controlled dataset of virtual patients with varying degrees and locations of
myocardial scar is used for training and validation. The proposed method
successfully detects and localizes the scars on 98% of the 5490 left ventricle
(LV) segments of the 305 patients in the test set using strain traces only. Due
to the sparse existence of scar, only 10% of the LV segments in the virtual
patient cohort have scar. Taking the imbalance into account, the class balanced
accuracy is calculated as 95%. The performance is reported on global,
territorial, and segmental levels. The proposed method proves successful on the
strain traces of the virtual cohort and offers the potential to solve the
regional myocardial scar detection problem on the strain traces of the real
patient cohorts.

通过应用一个基于卷积神经网络的分类算法，使用区域心肌应变的病理特征进行全局、区域和分段级别的心肌疾病诊断，并成功在测试集中对 5490 个左心室区段的 98% 进行了心肌疤痕的检测和定位，为解决心肌疤痕的检测问题提供了潜在解决方案。