A Multi-Scope Convolutional Neural Network for Automatic Left Ventricle Segmentation from Magnetic Resonance Images: Deep-Learning at Multiple Scopes

Cardiac Magnetic Resonance (CMR) imaging is widely used in the clinic to assess the patient-specific cardiac structure and function. However, the manual analysis of the CMR data is tedious and subjective. In this work, we developed a fully automatic segmentation system for the left ventricle (LV) myocardium from MR cine images. The system consists of three major components. Firstly, a conventional convolutional neural network (CNN) was trained to detect the global region of interest (ROI) of LV. Secondly, a novel multi-scope CNN was proposed to segment the LV myocardium from the reduced ROI, taking advantage of the image context in different scopes, such that both the local accuracy and global consistency can be implicitly learned by the CNN. Finally the results were pruned with simple morphological filtering preserving the largest component. With a relatively small training set of 200 MR cine images, the method achieved an average segmentation accuracy of 0.71 as expressed by the Dice overlap index. The proposed method can be applied to automatically segment the LV from MR images with the reasonable accuracy, or as a proper initialization for local shape methods to achieve further refined results.