Deep learning has achieved promising segmentation performance on 3D left atrium MR images. However, annotations for segmentation tasks are expensive, costly and difficult to obtain. In this paper, we introduce a novel hierarchical consistency regularized mean teacher framework for 3D left atrium segmentation. In each iteration, the student model is optimized by multi-scale deep supervision and hierarchical consistency regularization, concurrently. Extensive experiments have shown that our method achieves competitive performance as compared with full annotation, outperforming other state-of-the-art semi-supervised segmentation methods.
@article{arxiv.2105.10369,
title = {Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation},
author = {Shumeng Li and Ziyuan Zhao and Kaixin Xu and Zeng Zeng and Cuntai Guan},
journal= {arXiv preprint arXiv:2105.10369},
year = {2022}
}
Comments
Accepted in 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE EMBC 2021