A Two-Stage Fully Automatic Segmentation Scheme Using Both 2D and 3D U-Net for Multi-sequence Cardiac MR

Abstract

Multi-sequence cardiac magnetic resonance (MR) segmentation is an important medical imaging technology that facilitates intelligent interpretation of clinical MR images. However, fully automatic segmentation of multi-sequence cardiac MR is a challenging task due to the complexity and variability of cardiac anatomy. In this study, we propose a two-stage deep learning scheme for automatic segmentation of volumetric multi-sequence MR images by leveraging both 2D and 3D U-Net. In the first stage, a 2D U-Net model coupled with the iterative randomized Hough transform is employed on the balanced-steady state free precession (bSSFP) MR sequences, so as to find the center coordinates of the left ventricles (LVs). The regions of interest (ROIs) are then localized around the center coordinates on the corresponding late gadolinium enhanced (LGE) MR sequences. In the second stage, a 3D probabilistic U-Net model is performed on the ROIs in the LGE data to segment the LV, right ventricle (RV) and left ventricular myocardium (MYO). Experimental results on the MICCAI 2019 Multi-Sequence Cardiac MR Segmentation (MS-CMRSeg) Challenge show that the proposed scheme performs well with average Dice similarity coefficients of LV, RV and MYO as 0.792, 0.697 and 0.611, respectively.