Echocardiography Segmentation by Quality Translation Using Anatomically Constrained CycleGAN

Abstract

Segmentation of an echocardiogram (echo) is favorable for assessment of cardiac functionality and disease. The quality of the captured echo is a key factor that affects the segmentation accuracy. In this paper, we propose a novel generative adversarial network architecture, which aims to improve echo quality for the segmentation of the left ventricle (LV). The proposed model is anatomically constrained to the structure of the LV in apical four chamber (AP4) echo view. A set of discriminative features are learned through unpaired translation of low to high quality echo using adversarial training. The anatomical constraint regularizes the model during end-to-end training to preserve the corresponding shape of the LV in the translated echo. Experiments show that leveraging information in the translated high quality echocardiograms by the proposed method improves the robustness of the segmentation, where the worst-case Dice similarity score is improved by a margin of 15% over the baseline.