Fully Automatic Segmentation of the Left Ventricle Using Multi-Scale Fusion Learning

Abstract

Segmentation of the left ventricle (LV) is essential for quantitative calculation of clinical indices for analyzing the cardiac contractile function. However, it is challenging to automatically segment small-contour cardiac magnetic resonance (CMR) images for traditional convolutional neural networks (ConvNets) because of their low robustness to scale variation. In this paper, we propose a multi-scale fusion learning method to advance the performance of ConvNets for the LV segmentation. To realize our multi-scale fusion learning, single-scale input and multi-scale output (SIMO) networks are firstly trained to construct a SIMO-based multi-scale fusion network (SIMO-based MSF_Net). The trained SIMO networks produce different-scale coarse prediction results which are then fused into another multi-scale network. Finally, the coarse results are progressively refined to yield finer segmentation results. Our multi-scale fusion learning is evaluated on MICCAI 2009 challenging database for the LV segmentation. Experimental results demonstrate the robustness of our SIMO-based MSF_Net for the segmentation of challenging CMR images and the metric of “Good contours” achieves 98.35% on the testing set, which is greatly improved compared with the state-of-the-art methods.