Weakly Supervised Deformation Network for 3D Echocardiography Segmentation on Left Ventricle

Abstract

The automated 3D echocardiography segmentation on left ventricle (LV) is very important for clinical evaluation of LV function. However, the segmentation is difficult due to the 3D echocardiography's challenges, such as the low signal-to-noise ratio, indistinguishable boundaries between LV and other heart substructures, and limited annotation data. This paper aims to propose a novel method to achieve accurate 3D echocardiography segmentation on LV, based on a weakly supervised deformable network. The deformation network was optimized by generative adversarial constraint and volume similarity constraint. The proposed framework was trained and validated on 3D echocardiography datasets which including 70 patients (35 train patients and 35 test patients). The results demonstrated the proposed method is relatively accurate and has potential for further research and application.