Deep Regression Segmentation for Cardiac Bi-Ventricle MR Images

Abstract

Cardiac bi-ventricle segmentation can help physicians to obtain clinical indices, such as mass and volume of left ventricle (LV) and right ventricle (RV). In this paper, we propose a regression segmentation framework to delineate boundaries of bi-ventricle from cardiac magnetic resonance (MR) images by building a regression model automatically and accurately. First, we extract DAISY feature from images. Then, a point based representation method is employed to depict the boundaries. Finally, we use DAISY as input and boundary points as labels to train the regression model based on deep belief network. Regression combined deep learning and DAISY feature can capture high level image information and accurately segment biventricle with fewer assumptions and lower computational cost. In our experiment, the performance of the proposed framework is compared with manual segmentation on 145 clinical subjects (2900 images in total), which are collected from three hospitals affiliated with two health care centers (London Healthcare Center and St. Josephs HealthCare). The results of our method and manually segmented method are highly consistent. High Pearson's correlation coefficient between automated boundaries and manual annotation is up to 0.995 (endocardium of LV), 0.997 (epicardium of LV), and 0.985 (RV). Average Dice metric is up to 0.916 (endocardium of LV), 0.941 (epicardium of LV), and 0.844 (RV). Altogether, experimental results are capable of demonstrating the efficacy of our regression segmentation framework for cardiac MR images.