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U-Net and its variants for medical image segmentation: theory and applications

arXiv 2011.01118 ↗First posted 2 Nov 2020eess.IV cs.CV cs.LGcited by 169

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Abstract (arXiv)

U-net is an image segmentation technique developed primarily for medical image analysis that can precisely segment images using a scarce amount of training data. These traits provide U-net with a very high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in all major image modalities from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. As the potential of U-net is still increasing, in this review we look at the various developments that have been made in the U-net architecture and provide observations on recent trends. We examine the various innovations that have been made in deep learning and discuss how these tools facilitate U-net. Furthermore, we look at image modalities and application areas where U-net has been applied.

Key facts. Cited by 169 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Nov 2020.

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Citing papers per year: 2021: 8; 2022: 26; 2023: 39; 2024: 52; 2025: 27; 2026: 17.

169 papers.

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  1. 2022Medical Image Segmentation Review: The success of U-Netcited by 190
  2. 2023CellViT: Vision Transformers for Precise Cell Segmentation and Classificationcited by 108
  3. 2023SegVol: Universal and Interactive Volumetric Medical Image Segmentationcited by 86
  4. 2024How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Modelcited by 32
  5. 2023A Unified Framework for U-Net Design and Analysiscited by 27
  6. 2023Segment Anything Model (SAM) for Radiation Oncologycited by 24
  7. 2024Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Modelscited by 20
  8. 2025How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?cited by 19
  9. 2024U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Modelscited by 17
  10. 2021Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentationcited by 16
  11. 2024A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trendscited by 16
  12. 2023Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environmentscited by 14
38 more
  1. 2024SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRIcited by 14
  2. 2021Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Imagescited by 13
  3. 2022Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentationcited by 13
  4. 2023Topology-Aware Focal Loss for 3D Image Segmentationcited by 12
  5. 2023nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performancecited by 12
  6. 2025PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representationcited by 12
  7. 2022Robust Split Federated Learning for U-shaped Medical Image Networkscited by 11
  8. 2024SGSeg: Enabling Text-free Inference in Language-guided Segmentation of Chest X-rays via Self-guidancecited by 11
  9. 2022SS-3DCapsNet: Self-supervised 3D Capsule Networks for Medical Segmentation on Less Labeled Datacited by 10
  10. 2024Exploration of Multi-Scale Image Fusion Systems in Intelligent Medical Image Analysiscited by 10
  11. 2021PocketNet: A Smaller Neural Network for Medical Image Analysiscited by 9
  12. 2024A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attentioncited by 9
  13. 2024Applying Conditional Generative Adversarial Networks for Imaging Diagnosiscited by 9
  14. 2022Label-free segmentation from cardiac ultrasound using self-supervised learningcited by 8
  15. 2023Channel prior convolutional attention for medical image segmentationcited by 8
  16. 2023Metrically Scaled Monocular Depth Estimation through Sparse Priors for Underwater Robotscited by 8
  17. 2024OmniArch: Building Foundation Model For Scientific Computingcited by 8
  18. 2025Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Reviewcited by 8
  19. 2025Recent Advances in Medical Imaging Segmentation: A Surveycited by 8
  20. 2022Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Imagescited by 7
  21. 2024BRAU-Net++: U-Shaped Hybrid CNN-Transformer Network for Medical Image Segmentationcited by 7
  22. 2024Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Methodcited by 7
  23. 2024Explicit Differentiable Slicing and Global Deformation for Cardiac Mesh Reconstructioncited by 7
  24. 2022Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devicescited by 6
  25. 2023LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based CNN for Retinal Blood Vessel Segmentationcited by 6
  26. 2024MIPI 2024 Challenge on Demosaic for HybridEVS Camera: Methods and Resultscited by 6
  27. 2024A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizabilitycited by 6
  28. 2025Towards Operational Automated Greenhouse Gas Plume Detection and Delineationcited by 6
  29. 2025Alleviating Textual Reliance in Medical Language-guided Segmentation via Prototype-driven Semantic Approximationcited by 6
  30. 2021BP-Net: Efficient Deep Learning for Continuous Arterial Blood Pressure Estimation using Photoplethysmogramcited by 5
  31. 2022Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imagingcited by 5
  32. 2022Trichomonas Vaginalis Segmentation in Microscope Imagescited by 5
  33. 2023Forward-Forward Algorithm for Hyperspectral Image Classification: A Preliminary Studycited by 5
  34. 2024Back-in-Time Diffusion: Unsupervised Detection of Medical Deepfakescited by 5
  35. 2021Comparison of 2D vs. 3D U-Net Organ Segmentation in abdominal 3D CT imagescited by 4
  36. 2021Deep Learning for Reaction-Diffusion Glioma Growth Modelling: Towards a Fully Personalised Model?cited by 4
  37. 2022Application of belief functions to medical image segmentation: A reviewcited by 4
  38. 2023Reslicing Ultrasound Images for Data Augmentation and Vessel Reconstructioncited by 4

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