mlsanityData to 13 Oct 2026

A Comprehensive Survey of Medical Image Segmentation: Challenges, Benchmarks, and Beyond

arXiv 2606.16153 ↗First posted 15 Jun 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

Medical image segmentation plays a critical role in clinical diagnostics, treatment planning, disease monitoring, and neurological disorder identification. This article presents a comprehensive review of its systematic development, covering widely used public datasets, representative methods built on the U-Net, Transformer, and SAM architectures, and key evaluation metrics with their differences, followed by an analysis of major challenges from multiple perspectives. Unlike surveys that focus on a single model family or a specific clinical application, this review organizes U-Net-, Transformer-, and SAM-based methods within a unified analytical framework, with a particular focus on their effectiveness in improving segmentation accuracy and efficiency. This work aims to guide future research and support clinical translation of medical image segmentation, with all related resources publicly available in our GitHub repository: https://github.com/andrew-pengyu/Awsome_MedSeg/tree/main.

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References · 28

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,656
  2. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,287
  3. 2018ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentationcited by 623
  4. 2022MedCLIP: Contrastive Learning from Unpaired Medical Images and Textcited by 567
  5. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 507
  6. 2023Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentationcited by 487
  7. 2022Anomaly Detection via Reverse Distillation from One-Class Embeddingcited by 420
  8. 2019Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentationcited by 378
  9. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
  10. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  11. 2018A Probabilistic U-Net for Segmentation of Ambiguous Imagescited by 281
  12. 2023Customized Segment Anything Model for Medical Image Segmentationcited by 279
16 more
  1. 2020Semi-supervised Medical Image Segmentation through Dual-task Consistencycited by 215
  2. 2020Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Imagescited by 152
  3. 2023APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot ADcited by 129
  4. 2019Learning From Noisy Labels By Regularized Estimation Of Annotator Confusioncited by 126
  5. 2021Semi-supervised Left Atrium Segmentation with Mutual Consistency Trainingcited by 104
  6. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  7. 2020Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistencycited by 102
  8. 2022Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentationcited by 93
  9. 2016DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networkscited by 79
  10. 2022Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detectioncited by 72
  11. 2021Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentationcited by 61
  12. 2020Disentangling Human Error from the Ground Truth in Segmentation of Medical Imagescited by 58
  13. 2025Show and Segment: Universal Medical Image Segmentation via In-Context Learningcited by 16
  14. 2025Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentationcited by 7
  15. 2024Beyond Pixel-Wise Supervision for Medical Image Segmentation: From Traditional Models to Foundation Modelscited by 4
  16. 2024ContrastDiagnosis: Enhancing Interpretability in Lung Nodule Diagnosis Using Contrastive Learningcited by 1

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