mlsanityData to 14 Oct 2026

Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation

arXiv 2105.10369 ↗First posted 21 May 2021cs.CV cs.AI eess.IVcited by 21

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 21 May 2021.

Builds on

14 of 21 references.

References · 14

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  3. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  4. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
  5. 2017Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning resultscited by 472
  6. 2019Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentationcited by 379
  7. 2019Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentationcited by 260
  8. 2020Semi-supervised Medical Image Segmentation through Dual-task Consistencycited by 216
  9. 2018Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysiscited by 179
  10. 2020Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Imagescited by 152
  11. 2019Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentationcited by 140
  12. 2020Double-Uncertainty Weighted Method for Semi-supervised Learningcited by 44
2 more
  1. 2021DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentationcited by 27
  2. 2019Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentationcited by 6

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 9; 2023: 5; 2024: 3; 2025: 2.

21 papers.

Sort:

By area:

  1. 2022Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentationcited by 76
  2. 2022LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentationcited by 27
  3. 2021Medical Image Segmentation with 3D Convolutional Neural Networks: A Surveycited by 16
  4. 2022Translation Consistent Semi-supervised Segmentation for 3D Medical Imagescited by 13
  5. 2024Uncertainty-aware Medical Diagnostic Phrase Identification and Groundingcited by 13
  6. 2022Self-supervised Assisted Active Learning for Skin Lesion Segmentationcited by 7
  7. 2022MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentationcited by 7
  8. 2022Time Majority Voting, a PC-based EEG Classifier for Non-expert Userscited by 7
  9. 2023Improving Differentiable Architecture Search via Self-Distillationcited by 6
  10. 2023Semi-Supervised Crowd Counting with Contextual Modeling: Facilitating Holistic Understanding of Crowd Scenescited by 6
  11. 2022ACT-Net: Asymmetric Co-Teacher Network for Semi-supervised Memory-efficient Medical Image Segmentationcited by 4
  12. 2023MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentationcited by 4
9 more
  1. 2022Voxel-wise Adversarial Semi-supervised Learning for Medical Image Segmentationcited by 2
  2. 2023Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentationcited by 2
  3. 2024Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentationcited by 2
  4. 2021One-shot Weakly-Supervised Segmentation in Medical Imagescited by 1
  5. 2022Residual Channel Attention Network for Brain Glioma Segmentationcited by 1
  6. 2025crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023cited by 1
  7. 2023Diagonal Hierarchical Consistency Learning for Semi-supervised Medical Image Segmentationcited by 0
  8. 2024Cross-model Mutual Learning for Exemplar-based Medical Image Segmentationcited by 0
  9. 2025Dual Teacher-Student Learning for Semi-supervised Medical Image Segmentationcited by 0