Developing a deep learning method for medical segmentation tasks heavily
relies on a large amount of labeled data. However, the annotations require
professional knowledge and are limited in number. Recently, semi-supervised
learning has demonstrated great potential in medical segmentation tasks. Most
existing methods related to cardiac magnetic resonance images only focus on
regular images with similar domains and high image quality. A semi-supervised
domain generalization method was developed in [2], which enhances the quality
of pseudo labels on varied datasets. In this paper, we follow the strategy in
[2] and present a domain generalization method for semi-supervised medical
segmentation. Our main goal is to improve the quality of pseudo labels under
extreme MRI Analysis with various domains. We perform Fourier transformation on
input images to learn low-level statistics and cross-domain information. Then
we feed the augmented images as input to the double cross pseudo supervision
networks to calculate the variance among pseudo labels. We evaluate our method
on the CMRxMotion dataset [1]. With only partially labeled data and without
domain labels, our approach consistently generates accurate segmentation
results of cardiac magnetic resonance images with different respiratory
motions. Code is available at: https://github.com/MAWanqin2002/STACOM2022MaComment: Accepted by International Workshop on Statistical Atlases and
Computational Models of the Heart (STACOM2022) of MICCAI202
