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.
@article{arxiv.2012.14785,
title = {Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer},
author = {Yao Zhang and Jiawei Yang and Feng Hou and Yang Liu and Yixin Wang and Jiang Tian and Cheng Zhong and Yang Zhang and Zhiqiang He},
journal= {arXiv preprint arXiv:2012.14785},
year = {2022}
}
Comments
The method that won 2nd place in MICCAI 2020 MnM's Challenge