Ensemble of Convolutional Neural Networks for Heart Segmentation

Abstract

Training an ensemble of convolutional neural networks requires much computational resources for a large set of high-resolution medical 3D scans because deep representation requires many parameters and layers. In this study, 100 3D late gadolinium-enhanced (LGE)-MRIs with a spatial resolution of 0.625 mm × 0.625 mm × 0.625 mm from patients with atrial fibrillation were utilized. To contain cost of the training, down-sampling of images, transfer learning and ensemble of network’s past weights were deployed. This paper proposes an image processing stage using down-sampling and contrast limited adaptive histogram equalization, a network training stage using a cyclical learning rate schedule, and a testing stage using an ensemble. While this method achieves reasonable segmentation accuracy with the median of the Dice coefficients at 0.87, this method can be used on a computer with a GPU that has a Kepler architecture and at least 3 GB memory.