  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.
