Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer
This work provides an incremental improvement in cardiac image segmentation, which is important for doctors in diagnosing and treating heart diseases.
This paper addresses the challenge of cardiac image segmentation from MRI volumes, specifically for the left and right ventricle blood pools and left ventricular myocardium, despite limited annotations and data variance. Their method achieved 2nd place among 14 teams in the M&Ms challenge.
Accurate segmentation of cardiac structures can assist doctors to diagnose diseases, and to improve treatment planning, which is highly demanded in the clinical practice. However, the shortage of annotation and the variance of the data among different vendors and medical centers restrict the performance of advanced deep learning methods. In this work, we present a fully automatic method to segment cardiac structures including the left (LV) and right ventricle (RV) blood pools, as well as for the left ventricular myocardium (MYO) in MRI volumes. Specifically, we design a semi-supervised learning method to leverage unlabelled MRI sequence timeframes by label propagation. Then we exploit style transfer to reduce the variance among different centers and vendors for more robust cardiac image segmentation. We evaluate our method in the M&Ms challenge 7 , ranking 2nd place among 14 competitive teams.