mlsanityData to 14 Oct 2026

Medical Image Segmentation Using Deep Learning: A Survey

arXiv 2009.13120 ↗First posted 28 Sep 2020eess.IV cs.CVcited by 164

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Abstract (arXiv)

Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. In this paper, we present a comprehensive thematic survey on medical image segmentation using deep learning techniques. This paper makes two original contributions. Firstly, compared to traditional surveys that directly divide literatures of deep learning on medical image segmentation into many groups and introduce literatures in detail for each group, we classify currently popular literatures according to a multi-level structure from coarse to fine. Secondly, this paper focuses on supervised and weakly supervised learning approaches, without including unsupervised approaches since they have been introduced in many old surveys and they are not popular currently. For supervised learning approaches, we analyze literatures in three aspects: the selection of backbone networks, the design of network blocks, and the improvement of loss functions. For weakly supervised learning approaches, we investigate literature according to data augmentation, transfer learning, and interactive segmentation, separately. Compared to existing surveys, this survey classifies the literatures very differently from before and is more convenient for readers to understand the relevant rationale and will guide them to think of appropriate improvements in medical image segmentation based on deep learning approaches.

Key facts. Cited by 164 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Sep 2020.

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Citing papers per year: 2021: 9; 2022: 14; 2023: 27; 2024: 47; 2025: 46; 2026: 21.

164 papers.

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  1. 2023Customized Segment Anything Model for Medical Image Segmentationcited by 280
  2. 2024U-KAN Makes Strong Backbone for Medical Image Segmentation and Generationcited by 179
  3. 2024BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at oncecited by 111
  4. 2024A Comprehensive Survey of Foundation Models in Medicinecited by 56
  5. 2023AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challengecited by 35
  6. 2024Image Segmentation in Foundation Model Era: A Surveycited by 35
  7. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 33
  8. 2024How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Modelcited by 32
  9. 2021iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Imagescited by 28
  10. 2023A Recent Survey of Vision Transformers for Medical Image Segmentationcited by 23
  11. 2024FedFMS: Exploring Federated Foundation Models for Medical Image Segmentationcited by 21
  12. 2021Modality specific U-Net variants for biomedical image segmentation: A surveycited by 20
38 more
  1. 2023Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Samplingcited by 19
  2. 2024FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classificationcited by 19
  3. 2021Using Soft Labels to Model Uncertainty in Medical Image Segmentationcited by 18
  4. 2021A survey on shape-constraint deep learning for medical image segmentationcited by 16
  5. 2025Show and Segment: Universal Medical Image Segmentation via In-Context Learningcited by 16
  6. 2022MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentationcited by 13
  7. 2022MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentationcited by 13
  8. 2021BT-Unet: A self-supervised learning framework for biomedical image segmentation using Barlow Twins with U-Net modelscited by 12
  9. 2023SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentationcited by 12
  10. 2024Clustering Propagation for Universal Medical Image Segmentationcited by 12
  11. 2024CSWin-UNet: Transformer UNet with Cross-Shaped Windows for Medical Image Segmentationcited by 12
  12. 2026INSID3: Training-Free In-Context Segmentation with DINOv3cited by 12
  13. 2024S-SAM: SVD-based Fine-Tuning of Segment Anything Model for Medical Image Segmentationcited by 11
  14. 2024A Survey of Medical Vision-and-Language Applications and Their Techniquescited by 11
  15. 2022SS-3DCapsNet: Self-supervised 3D Capsule Networks for Medical Segmentation on Less Labeled Datacited by 10
  16. 2024SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-promptingcited by 9
  17. 2022Vector Quantisation for Robust Segmentationcited by 8
  18. 2023Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Modelcited by 8
  19. 2025Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Reviewcited by 8
  20. 2025MediSee: Reasoning-based Pixel-level Perception in Medical Imagescited by 8
  21. 2022Graph Flow: Cross-layer Graph Flow Distillation for Dual Efficient Medical Image Segmentationcited by 7
  22. 2022MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentationcited by 7
  23. 2023Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learningcited by 6
  24. 2023DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion modelscited by 6
  25. 2023Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentationcited by 6
  26. 2024Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domaincited by 6
  27. 2024FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentationcited by 6
  28. 2024SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text Cuescited by 6
  29. 2024Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignmentscited by 6
  30. 2023A Recycling Training Strategy for Medical Image Segmentation with Diffusion Denoising Modelscited by 5
  31. 2023Certification of Deep Learning Models for Medical Image Segmentationcited by 5
  32. 2023FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scalingcited by 5
  33. 2023CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classificationcited by 5
  34. 2025Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentationcited by 5
  35. 2025Enhancing Fetal Plane Classification Accuracy with Data Augmentation Using Diffusion Modelscited by 5
  36. 2025Conditional diffusion model with spatial attention and latent embedding for medical image segmentationcited by 5
  37. 2022Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task Deep Network for Medical Image Semantic Segmentationcited by 4
  38. 2023AttResDU-Net: Medical Image Segmentation Using Attention-based Residual Double U-Netcited by 4

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