mlsanityData to 13 Oct 2026

From CNN to Transformer: A Review of Medical Image Segmentation Models

arXiv 2308.05305 ↗First posted 10 Aug 2023eess.IV cs.CV cs.LGcited by 22

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Abstract (arXiv)

Medical image segmentation is an important step in medical image analysis, especially as a crucial prerequisite for efficient disease diagnosis and treatment. The use of deep learning for image segmentation has become a prevalent trend. The widely adopted approach currently is U-Net and its variants. Additionally, with the remarkable success of pre-trained models in natural language processing tasks, transformer-based models like TransUNet have achieved desirable performance on multiple medical image segmentation datasets. In this paper, we conduct a survey of the most representative four medical image segmentation models in recent years. We theoretically analyze the characteristics of these models and quantitatively evaluate their performance on two benchmark datasets (i.e., Tuberculosis Chest X-rays and ovarian tumors). Finally, we discuss the main challenges and future trends in medical image segmentation. Our work can assist researchers in the related field to quickly establish medical segmentation models tailored to specific regions.

Key facts. Cited by 22 papers in the map (data through 13 Oct 2026). Builds on 25 of its 42 references that are in the map. First posted on arXiv 10 Aug 2023.

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25 of 42 references.

References · 25

  1. 2017Attention Is All You Needcited by 60,106
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,588
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,656
  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,111
  5. 2023Segment Anythingcited by 8,195
  6. 2020End-to-End Object Detection with Transformerscited by 7,396
  7. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,364
  8. 2016Densely Connected Convolutional Networkscited by 6,707
  9. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,849
  10. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,240
  11. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,216
  12. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,287
13 more
  1. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,159
  2. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,554
  3. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,522
  4. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,484
  5. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,475
  6. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,219
  7. 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,129
  8. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 720
  9. 2014Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFscited by 553
  10. 2018Attention Gated Networks: Learning to Leverage Salient Regions in Medical Imagescited by 271
  11. 2019Deep Semantic Segmentation of Natural and Medical Images: A Reviewcited by 125
  12. 2017Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networkscited by 28
  13. 2016Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasoundcited by 17

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Citing papers per year: 2024: 6; 2025: 7; 2026: 9.

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  1. 2024SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images using a Two-Phase Approach to Multi-class Semantic and Instance Segmentationcited by 9
  2. 2024CAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentationcited by 4
  3. 2026Diffusion Model as a Generalist Segmentation Learnercited by 4
  4. 2024Architecture Analysis and Benchmarking of 3D U-shaped Deep Learning Models for Thoracic Anatomical Segmentationcited by 2
  5. 2024Benchmarking Retinal Blood Vessel Segmentation Models for Cross-Dataset and Cross-Disease Generalizationcited by 2
  6. 2025GeloVec: Higher Dimensional Geometric Smoothing for Coherent Visual Feature Extraction in Image Segmentationcited by 2
  7. 2025MSA-UNet3+: Multi-Scale Attention UNet3+ with New Supervised Prototypical Contrastive Loss for Coronary DSA Image Segmentationcited by 1
  8. 2025CARE: Anti-entanglement Ultrasound Image Segmentation via Channel-Aware Region Extricationcited by 1
  9. 2025SAMRI: Segment Any MRIcited by 1
  10. 2026Three-dimensional visualization of X-ray micro-CT with large-scale datasets: Efficiency and accuracy for real-time interactioncited by 1
  11. 2024Judging from Support-set: A New Way to Utilize Few-Shot Segmentation for Segmentation Refinement Processcited by 0
  12. 2024Nd-BiMamba2: A Unified Bidirectional Architecture for Multi-Dimensional Data Processingcited by 0
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  1. 2025Large model retrieval enhancement framework for construction site risk identificationcited by 0
  2. 2025Improved Segmentation of Polyps and Visual Explainability Analysiscited by 0
  3. 2025Comparative Study of UNet-based Architectures for Liver Tumor Segmentation in Multi-Phase Contrast-Enhanced Computed Tomographycited by 0
  4. 2026Rewis3d: Reconstruction Improves Weakly-Supervised Semantic Segmentationcited by 0
  5. 2026Decoding Matters: Efficient Mamba-Based Decoder with Distribution-Aware Deep Supervision for Medical Image Segmentationcited by 0
  6. 2026Exploring the Impact of Skin Color on Skin Lesion Segmentationcited by 0
  7. 2026Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detectioncited by 0
  8. 2026AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentationcited by 0
  9. 2026ESRVS: Extreme Semi-Supervised Retinal Vessel Segmentation with a Single Annotated Imagecited by 0
  10. 2026Enhancing Low Back Pain Assessment with Diffusion Models for Lumbar Spine MRI Segmentationcited by 0