Fully automated segmentation of left ventricular myocardium from 3D late gadolinium enhancement magnetic resonance images using a U-net convolutional neural network-based model

Myocardial tissue characterization on 3-dimensional late gadolinium enhancement magnetic resonance (3D LGE MR) is of increasing clinical importance for the quantification and spatial mapping of myocardial scar, a recognized substrate for malignant ventricular arrhythmias. Success of this task is dependent upon reproducible segmentation of myocardial architecture in 3D-space. In this paper, we describe a novel method to segment left ventricle (LV) myocardium from 3D LGE MR images using a U-Net convolutional neural network (CNN)-based model. Our proposed network consists of shrinking and expanding paths, where image features are captured and localized through several convolutional, pooling and up-sampling layers. We trained our model using 2090 slices extracted and artificially augmented from 14 3D LGE MR datasets, followed by validation of the trained model on ten 3D LGE MR unobserved test datasets inclusive of 926 slices. Averages of Dice index (DI) and absolute volume difference as a percentage versus manual defined myocardial volumes (NAVD) on the test dataset were obtained, providing values of 86.61 ± 3.80 % and 12.95 ± 9.56%, respectively. These algorithm-generated results demonstrate usefulness of our proposed fully automated method for segmentation of the LV myocardium from 3D LGE MR images.