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Deep learning for cardiac image segmentation: A review

arXiv 1911.03723 ↗First posted 9 Nov 2019eess.IV cs.CV cs.LGcited by 131

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Abstract (arXiv)

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research.

Key facts. Cited by 131 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Nov 2019.

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Citing papers per year: 2021: 17; 2022: 22; 2023: 27; 2024: 25; 2025: 25; 2026: 15.

131 papers.

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  1. 2023SegVol: Universal and Interactive Volumetric Medical Image Segmentationcited by 87
  2. 2021Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentationcited by 36
  3. 2021Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Reviewcited by 26
  4. 2021Medical Image Segmentation with Limited Supervision: A Review of Deep Network Modelscited by 22
  5. 2021A persistent homology-based topological loss for CNN-based multi-class segmentation of CMRcited by 21
  6. 2021Modality specific U-Net variants for biomedical image segmentation: A surveycited by 20
  7. 2022Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learningcited by 20
  8. 2022Multi-Modality Cardiac Image Computing: A Surveycited by 19
  9. 2023Dehazing Ultrasound using Diffusion Modelscited by 17
  10. 2022Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modelingcited by 16
  11. 2022Domain-incremental Cardiac Image Segmentation with Style-oriented Replay and Domain-sensitive Feature Whiteningcited by 16
  12. 2023Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance imagescited by 16
38 more
  1. 2021A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiogramscited by 15
  2. 2024UU-Mamba: Uncertainty-aware U-Mamba for Cardiac Image Segmentationcited by 15
  3. 2025Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyondcited by 14
  4. 2022The Extreme Cardiac MRI Analysis Challenge under Respiratory Motion (CMRxMotion)cited by 12
  5. 2022vMFNet: Compositionality Meets Domain-generalised Segmentationcited by 11
  6. 2022A systematic study of race and sex bias in CNN-based cardiac MR segmentationcited by 9
  7. 2024Continual Learning in Medical Imaging: A Survey and Practical Analysiscited by 9
  8. 2024Whole Heart 3D+T Representation Learning Through Sparse 2D Cardiac MR Imagescited by 9
  9. 2023Motion-compensated MR CINE reconstruction with reconstruction-driven motion estimationcited by 8
  10. 2021Recent Advances in Fibrosis and Scar Segmentation from Cardiac MRI: A State-of-the-Art Review and Future Perspectivescited by 7
  11. 2022Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentationcited by 7
  12. 2022MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentationcited by 7
  13. 2023Full or Weak annotations? An adaptive strategy for budget-constrained annotation campaignscited by 7
  14. 2024Segmenting Medical Images: From UNet to Res-UNet and nnUNetcited by 7
  15. 2022A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT imagescited by 6
  16. 2021Optimized Automated Cardiac MR Scar Quantification with GAN-Based Data Augmentationcited by 5
  17. 2021Domain generalization in deep learning for contrast-enhanced imagingcited by 4
  18. 2022An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentationcited by 4
  19. 2023Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentationcited by 4
  20. 2023Assessment of Deep Learning Segmentation for Real-Time Free-Breathing Cardiac Magnetic Resonance Imaging at Rest and Under Exercise Stresscited by 4
  21. 2024CAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentationcited by 4
  22. 2025Cardiac Digital Twins at Scale from MRI: Open Tools and Representative Models from ~55000 UK Biobank Participantscited by 4
  23. 2021Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptationcited by 3
  24. 2021Transfer Learning U-Net Deep Learning for Lung Ultrasound Segmentationcited by 3
  25. 2021Efficient Multi-Organ Segmentation Using SpatialConfiguration-Net with Low GPU Memory Requirementscited by 3
  26. 2022Shape-consistent Generative Adversarial Networks for multi-modal Medical segmentation mapscited by 3
  27. 2022A Study on the Use of Edge TPUs for Eye Fundus Image Segmentationcited by 3
  28. 2022Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challengescited by 3
  29. 2023Deep Conditional Shape Models for 3D cardiac image segmentationcited by 3
  30. 2024An Uncertainty-Aware Generalization Framework for Cardiovascular Image Segmentationcited by 3
  31. 2025Generative augmentations for improved cardiac ultrasound segmentation using diffusion modelscited by 3
  32. 2025Self-Supervised Learning for Image Segmentation: A Comprehensive Surveycited by 3
  33. 2025Robust Deep Learning for Myocardial Scar Segmentation in Cardiac MRI with Noisy Labelscited by 3
  34. 2021Two layer Ensemble of Deep Learning Models for Medical Image Segmentationcited by 2
  35. 2022Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligencecited by 2
  36. 2022FAS-UNet: A Novel FAS-driven Unet to Learn Variational Image Segmentationcited by 2
  37. 2023Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotationcited by 2
  38. 2023Ensemble Learning of Myocardial Displacements for Myocardial Infarction Detection in Echocardiographycited by 2

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