3D LV Probabilistic Segmentation in Cardiac MRI Using Generative Adversarial Network

Abstract

Cardiac magnetic resonance imaging (MRI) is one of the most useful techniques to understand and measure cardiac functions. Given the MR image data, segmentation of the left ventricle (LV) myocardium is the most common task to be addressed for recovering and studying LV wall motion. However, most of segmentation methods heavily rely on the imaging appearance for extracting the myocardial contours (epi- and endo-cardium). These methods cannot guarantee a consistent volume of the heart wall during cardiac cycle in reconstructed 3D LV wall models, which is contradictory to the assumption of approximately constant myocardial tissue. In the paper, we propose a probability-based segmentation method to estimate the probabilities of boundary pixels in the trabeculated region near the solid wall belonging to blood or muscle. It helps avoid artifactually moving the endocardium boundary inward during systole, as commonly happens with simple threshold-based segmentation methods. Our method takes s stack of 2D cine MRI slices as input, and produces the 3D probabilistic segmentation of the heart wall using a generative adversarial network (GAN). Based on numerical experiments, our proposed method outperformed the baseline method in terms of evaluation metrics on a synthetic dataset. Moreover, we achieved very good quality reconstructed results with on a real 2D cine MRI dataset (there is no truly independent 3D ground truth). The proposed approach helps to achieve better understanding cardiovascular motion. Moreover, it is the first attempt to use probabilistic segmentation of LV myocardium for 3D heart wall reconstruction from 2D cardiac cine MRI data, to the best of our knowledge.