# Medical Image Segmentation Using Deep Learning: A Survey

arXiv: https://arxiv.org/abs/2009.13120 · HTML: https://mlsanity.com/paper/2009.13120/

## Key facts

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

## Built on by (most cited first, 30 of 164)

- [Customized Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.13785/) (2023)
- [U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation](https://mlsanity.com/paper/2406.02918/) (2024)
- [BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once](https://mlsanity.com/paper/2405.12971/) (2024)
- [A Comprehensive Survey of Foundation Models in Medicine](https://mlsanity.com/paper/2406.10729/) (2024)
- [AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge](https://mlsanity.com/paper/2302.01738/) (2023)
- [Image Segmentation in Foundation Model Era: A Survey](https://mlsanity.com/paper/2408.12957/) (2024)
- [Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives](https://mlsanity.com/paper/2206.01136/) (2022)
- [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)
- [iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Images](https://mlsanity.com/paper/2112.11325/) (2021)
- [A Recent Survey of Vision Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2312.00634/) (2023)
- [FedFMS: Exploring Federated Foundation Models for Medical Image Segmentation](https://mlsanity.com/paper/2403.05408/) (2024)
- [Modality specific U-Net variants for biomedical image segmentation: A survey](https://mlsanity.com/paper/2107.04537/) (2021)
- [Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling](https://mlsanity.com/paper/2309.12378/) (2023)
- [FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification](https://mlsanity.com/paper/2407.08813/) (2024)
- [Using Soft Labels to Model Uncertainty in Medical Image Segmentation](https://mlsanity.com/paper/2109.12622/) (2021)
- [A survey on shape-constraint deep learning for medical image segmentation](https://mlsanity.com/paper/2101.07721/) (2021)
- [Show and Segment: Universal Medical Image Segmentation via In-Context Learning](https://mlsanity.com/paper/2503.19359/) (2025)
- [MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentation](https://mlsanity.com/paper/2205.04846/) (2022)
- [MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation](https://mlsanity.com/paper/2210.08168/) (2022)
- [BT-Unet: A self-supervised learning framework for biomedical image segmentation using Barlow Twins with U-Net models](https://mlsanity.com/paper/2112.03916/) (2021)
- [SPPNet: A Single-Point Prompt Network for Nuclei Image Segmentation](https://mlsanity.com/paper/2308.12231/) (2023)
- [Clustering Propagation for Universal Medical Image Segmentation](https://mlsanity.com/paper/2403.16646/) (2024)
- [CSWin-UNet: Transformer UNet with Cross-Shaped Windows for Medical Image Segmentation](https://mlsanity.com/paper/2407.18070/) (2024)
- [INSID3: Training-Free In-Context Segmentation with DINOv3](https://mlsanity.com/paper/2603.28480/) (2026)
- [S-SAM: SVD-based Fine-Tuning of Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2408.06447/) (2024)
- [A Survey of Medical Vision-and-Language Applications and Their Techniques](https://mlsanity.com/paper/2411.12195/) (2024)
- [SS-3DCapsNet: Self-supervised 3D Capsule Networks for Medical Segmentation on Less Labeled Data](https://mlsanity.com/paper/2201.05905/) (2022)
- [SAM-MPA: Applying SAM to Few-shot Medical Image Segmentation using Mask Propagation and Auto-prompting](https://mlsanity.com/paper/2411.17363/) (2024)
- [Vector Quantisation for Robust Segmentation](https://mlsanity.com/paper/2207.01919/) (2022)
- [Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model](https://mlsanity.com/paper/2305.09447/) (2023)

## 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.