Li, S., Zhao, Z., Xu, K., Zeng, Z., & Guan, C. (2021). Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). doi:10.1109/embc46164.2021.9629941
Abstract:
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.
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Publisher Copyright
Funding Info:
This research is supported by core funding from: Institute for Infocomm Research
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