Pseudo-3D Network for Multi-sequence Cardiac MR Segmentation

Abstract

Deep learning approaches have been regarded as a powerful model for cardiac magnetic resonance (CMR) image segmentation. However, most current deep learning approaches do not fully utilize the information from multi-sequence (MS) cardiac magnetic resonance. In this work, the deep learning method is used to fully-automatic segment the MS CMR data. The balanced-Steady State Free Precession (bSSFP) cine sequence is used to perform left ventricular positioning as a priori knowledge, and then the Late Gadolinium Enhancement (LGE) cine sequence is used for precise segmentation. This segmentation strategy makes full use of the complementary information from the MS CMR data. Moreover, to solve the anisotropy of volumetric medical images, we employ the Pseudo-3D convolution neural network structure to segment the LGE CMR data, which combines the advantage of 2D networks and preserving the spatial structure information in 3D data without compromising segmentation accuracy. Experimental results of the Multi-sequence Cardiac MR Segmentation Challenge (MS-CMRSeg 2019) show that our approach has achieved gratifying results even with limited GPU computing resources and small amounts of annotated data. The full implementation and configuration files in this article are available at https://github.com/liut969/Multi-sequence-Cardiac-MR-Segmentation.