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

arXiv: https://arxiv.org/abs/2011.01118 · HTML: https://mlsanity.com/paper/2011.01118/

## Key facts

- Cited by 169 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 30 of 169)

- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [CellViT: Vision Transformers for Precise Cell Segmentation and Classification](https://mlsanity.com/paper/2306.15350/) (2023)
- [SegVol: Universal and Interactive Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2311.13385/) (2023)
- [How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model](https://mlsanity.com/paper/2404.09957/) (2024)
- [A Unified Framework for U-Net Design and Analysis](https://mlsanity.com/paper/2305.19638/) (2023)
- [Segment Anything Model (SAM) for Radiation Oncology](https://mlsanity.com/paper/2306.11730/) (2023)
- [Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Models](https://mlsanity.com/paper/2408.08813/) (2024)
- [How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?](https://mlsanity.com/paper/2501.11253/) (2025)
- [U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models](https://mlsanity.com/paper/2404.18444/) (2024)
- [Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentation](https://mlsanity.com/paper/2112.11065/) (2021)
- [A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends](https://mlsanity.com/paper/2402.15490/) (2024)
- [Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments](https://mlsanity.com/paper/2312.07935/) (2023)
- [SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI](https://mlsanity.com/paper/2401.12974/) (2024)
- [Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images](https://mlsanity.com/paper/2109.10778/) (2021)
- [Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentation](https://mlsanity.com/paper/2209.07193/) (2022)
- [Topology-Aware Focal Loss for 3D Image Segmentation](https://mlsanity.com/paper/2304.12223/) (2023)
- [nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance](https://mlsanity.com/paper/2309.16967/) (2023)
- [PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representation](https://mlsanity.com/paper/2506.10351/) (2025)
- [Robust Split Federated Learning for U-shaped Medical Image Networks](https://mlsanity.com/paper/2212.06378/) (2022)
- [SGSeg: Enabling Text-free Inference in Language-guided Segmentation of Chest X-rays via Self-guidance](https://mlsanity.com/paper/2409.04758/) (2024)
- [SS-3DCapsNet: Self-supervised 3D Capsule Networks for Medical Segmentation on Less Labeled Data](https://mlsanity.com/paper/2201.05905/) (2022)
- [Exploration of Multi-Scale Image Fusion Systems in Intelligent Medical Image Analysis](https://mlsanity.com/paper/2406.18548/) (2024)
- [PocketNet: A Smaller Neural Network for Medical Image Analysis](https://mlsanity.com/paper/2104.10745/) (2021)
- [OmniArch: Building Foundation Model For Scientific Computing](https://mlsanity.com/paper/2402.16014/) (2024)
- [A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention](https://mlsanity.com/paper/2405.00130/) (2024)
- [Applying Conditional Generative Adversarial Networks for Imaging Diagnosis](https://mlsanity.com/paper/2408.02074/) (2024)
- [Label-free segmentation from cardiac ultrasound using self-supervised learning](https://mlsanity.com/paper/2210.04979/) (2022)
- [Channel prior convolutional attention for medical image segmentation](https://mlsanity.com/paper/2306.05196/) (2023)
- [Metrically Scaled Monocular Depth Estimation through Sparse Priors for Underwater Robots](https://mlsanity.com/paper/2310.16750/) (2023)
- [Explicit Differentiable Slicing and Global Deformation for Cardiac Mesh Reconstruction](https://mlsanity.com/paper/2409.02070/) (2024)

## 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.