Radiologic diagnosis of myocardial late gadolinium enhancement (LGE) is often represented as a description of lesion characteristics and distributions in the standard 16- (or 17-) segment myocardial model. In this study, we used short-axis LGE images and 16-segment labeled results from 66 patients with coronary artery disease and non-ischemic heart disease and trained a deep convolutional neural network (CNN) model. Short-axis images were transformed to polar coordinates after identification of the LV center point and anterior RV insertion point. The proposed method does not require manual delineation of the lesions and potentially enables automatic diagnosis of myocardial viability.
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