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Detection and Classification of Myocardial Delayed Enhancement Patterns on MR Images with Deep Neural Networks: A
Yasutoshi Ohta1, Hiroto Yunaga1, Shinichiro Kitao1
1Division of Radiology, Department of Pathophysiological Therapeutic Science, Tottori University Faculty of Medicine, Yonago City, Tottori 683-8504, Japan (Y.O., H.Y., S.K.); Department of Radiology, National Cerebral and Cardiovascular Center, Suita City, Osaka, Japan (Y.O., T.F.); and Department of Radiology, Kurashiki Heisei Hospital, Kurashiki City, Okayama, Japan (T.O.).
Purpose:
To evaluate whether deep neural networks trained on a similar number of images to that required during physician training in the American College of Cardiology Core Cardiovascular Training Statement can acquire the capability to detect and classify myocardial delayed enhancement (MDE) patterns.
Materials And Methods:
The authors retrospectively evaluated 1995 MDE images for training and validation of a deep neural network. Images were from 200 consecutive patients who underwent cardiovascular MRI and were obtained from the institutional database. Experienced cardiac MR image readers classified the images as showing the following MDE patterns: no pattern, epicardial enhancement, subendocardial enhancement, midwall enhancement, focal enhancement, transmural enhancement, and nondiagnostic. Data were divided into training and validation datasets by using a fourfold cross-validation method. Three untrained deep neural network architectures using the convolutional neural network (CNN) technique were trained with the training dataset images. The detection and classification accuracies of the trained CNNs were calculated with validation data.
Results:
The 1995 MDE images were classified by human readers as follows: no pattern, 926; epicardial enhancement, 91; subendocardial enhancement, 458; midwall enhancement, 118; focal enhancement, 141; transmural enhancement, 190; and nondiagnostic, 71. GoogLeNet, AlexNet, and ResNet-152 CNNs demonstrated accuracies of 79.5% (1592 of 1995 images), 78.9% (1574 of 1995 images), and 82.1% (1637 of 1995 images), respectively.
Conclusion:
Deep learning with CNNs using a limited amount of training data, less than that required during physician training, achieved high diagnostic performance in the detection of MDE on MR images.© RSNA, 2019Supplemental material is available for this article.