mlsanityData to 14 Oct 2026

Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy

arXiv 2311.13964 ↗First posted 23 Nov 2023eess.IV cs.AI cs.CVcited by 27

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Abstract (arXiv)

Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or masks and allows for iterative refinement of the model output so as to efficiently guide the system towards the desired behavior. In recent years, deep learning-based approaches have propelled results to a new level causing a rapid growth in the field with 121 methods proposed in the medical imaging domain alone. In this review, we provide a structured overview of this emerging field featuring a comprehensive taxonomy, a systematic review of existing methods, and an in-depth analysis of current practices. Based on these contributions, we discuss the challenges and opportunities in the field. For instance, we find that there is a severe lack of comparison across methods which needs to be tackled by standardized baselines and benchmarks.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). Builds on 84 of its 193 references that are in the map. First posted on arXiv 23 Nov 2023.

Builds on

84 of 193 references.

References · 84

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2023Segment Anythingcited by 8,234
  4. 2017Squeeze-and-Excitation Networkscited by 4,789
  5. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
  6. 2022Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Imagescited by 830
  7. 2021The Medical Segmentation Decathloncited by 628
  8. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 509
  9. 2023Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentationcited by 490
  10. 2023Segment Anything Model for Medical Image Analysis: an Experimental Studycited by 291
  11. 2023CLIP-Driven Universal Model for Organ Segmentation and Tumor Detectioncited by 224
  12. 2022Metrics reloaded: Recommendations for image analysis validationcited by 195
38 more
  1. 2023Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imagingcited by 161
  2. 2016Deep Interactive Object Selectioncited by 160
  3. 2019Segmentation-Based Deep-Learning Approach for Surface-Defect Detectioncited by 145
  4. 2017Deep Extreme Cut: From Extreme Points to Object Segmentationcited by 127
  5. 2020f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentationcited by 117
  6. 2017Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuningcited by 111
  7. 2017DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentationcited by 110
  8. 2017Annotating Object Instances with a Polygon-RNNcited by 88
  9. 2023SAM on Medical Images: A Comprehensive Study on Three Prompt Modescited by 82
  10. 2016DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networkscited by 79
  11. 2023Polyp-SAM: Transfer SAM for Polyp Segmentationcited by 64
  12. 2023SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Modelcited by 61
  13. 2023Understanding metric-related pitfalls in image analysis validationcited by 58
  14. 2018Iteratively Trained Interactive Segmentationcited by 52
  15. 2023When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentationcited by 52
  16. 2023AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weekscited by 52
  17. 2023Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentationcited by 51
  18. 2018Learning to Segment Medical Images with Scribble-Supervision Alonecited by 48
  19. 2020NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Imagescited by 46
  20. 2021MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learningcited by 45
  21. 2019Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learningcited by 41
  22. 2022MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Imagescited by 39
  23. 2020Going to Extremes: Weakly Supervised Medical Image Segmentationcited by 38
  24. 2023One-Prompt to Segment All Medical Imagescited by 38
  25. 2023SkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Modelcited by 32
  26. 2019Interactive segmentation of medical images through fully convolutional neural networkscited by 29
  27. 2021iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Imagescited by 28
  28. 2023SAM vs BET: A Comparative Study for Brain Extraction and Segmentation of Magnetic Resonance Images using Deep Learningcited by 24
  29. 2023SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspectivecited by 24
  30. 2023Segment Anything Model (SAM) for Radiation Oncologycited by 24
  31. 2023Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?cited by 22
  32. 2023Why is the winner the best?cited by 21
  33. 2023Learnable Ophthalmology SAMcited by 21
  34. 2018A Brief Survey and an Application of Semantic Image Segmentation for Autonomous Drivingcited by 19
  35. 2023GazeSAM: What You See is What You Segmentcited by 19
  36. 2020Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slicescited by 16
  37. 2023The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planningcited by 15
  38. 2018Iterative Interaction Training for Segmentation Editing Networkscited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 9; 2025: 8; 2026: 10.

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  1. 2024Interactive Medical Image Segmentation: A Benchmark Dataset and Baselinecited by 30
  2. 2024Interactive 3D Medical Image Segmentation with SAM 2cited by 17
  3. 2025Diffusion-empowered AutoPrompt MedSAMcited by 10
  4. 2024MedUHIP: Towards Human-In-the-Loop Medical Segmentationcited by 5
  5. 2024MSDNet: Multi-Scale Decoder for Few-Shot Semantic Segmentation via Transformer-Guided Prototypingcited by 5
  6. 2024Strike the Balance: On-the-Fly Uncertainty based User Interactions for Long-Term Video Object Segmentationcited by 4
  7. 2024RadioActive: 3D Radiological Interactive Segmentation Benchmarkcited by 4
  8. 2024Efficient MedSAMs: Segment Anything in Medical Images on Laptopcited by 4
  9. 2025Is Visual in-Context Learning for Compositional Medical Tasks within Reach?cited by 3
  10. 2024Rethinking Annotator Simulation: Realistic Evaluation of Whole-Body PET Lesion Interactive Segmentation Methodscited by 2
  11. 2026IMPACT-CYCLE: A Contract-Based Multi-Agent System for Claim-Level Supervisory Correction of Long-Video Semantic Memorycited by 2
  12. 2026Whole-Slide Image Analysis under Realistic Few-Shot Annotation Protocolscited by 2
15 more
  1. 2024Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancercited by 1
  2. 2025Benchmarking Feature Upsampling Methods for Vision Foundation Models using Interactive Segmentationcited by 1
  3. 2026From Static to Interactive: Adapting Visual in-Context Learners for User-Driven Taskscited by 1
  4. 2025EU-Nets: Enhanced, Explainable and Parsimonious U-Netscited by 0
  5. 2025RefCut: Interactive Segmentation with Reference Guidancecited by 0
  6. 2025Weakly Supervised Object Segmentation by Background Conditional Divergencecited by 0
  7. 2025MOIS-SAM2: Exemplar-based Segment Anything Model 2 for multilesion interactive segmentation of neurofibromas in whole-body MRIcited by 0
  8. 2025The 1st Solution for CARE Liver Task Challenge 2025: Contrast-Aware Semi-Supervised Segmentation with Domain Generalization and Test-Time Adaptationcited by 0
  9. 2026VLM-Guided Iterative Refinement for Surgical Image Segmentation with Foundation Modelscited by 0
  10. 2026CataractSAM-2: A Domain-Adapted Model for Anterior Segment Surgery Segmentation and Scalable Ground-Truth Annotationcited by 0
  11. 2026MedVol-R1: Reward-Driven Evidence Grounding for Volumetric Reasoning Segmentationcited by 0
  12. 2026Three-Phase Scribble-Adaptive Curriculum Learning for autoPETV Grand Challengecited by 0
  13. 2026IMVS: Interactive Medical Volume Segmentation with Test-Time Adaptation - A New Method for Annotating Radiology Datasetscited by 0
  14. 2026Confidence-Aware Teacher-Student Distillation for 3D Medical Segmentationcited by 0
  15. 2026From Human Corrections to Learned Segmentations: A Critical Mathematical Review of Information, Learning, and Human Effortcited by 0