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Abstract #2231

Segmental assessment of myocardial late gadolinium enhancement based on weakly supervised learning

Yoon-Chul Kim1 and Yeon Hyeon Choe2
1Clinical Research Institute, Samsung Medical Center, Sungkyunkwan Univ. School of Medicine, Seoul, Korea, Republic of, 2Department of Radiology, Samsung Medical Center, Sungkyunkwan Univ. School of Medicine, Seoul, Korea, Republic of

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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