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Medical Image Segmentation Methods: A Decision-Guided Survey Covering 2D/3D CNNs, Transformers, VLMs, SAM-Based

Kadir Sabanci1, Busra Aslan2, Muhammet Fatih Aslan3

  • 1Department of Electrical-Electronic Engineering, Karamanoglu Mehmetbey University, Karaman 70100, Türkiye.

Bioengineering (Basel, Switzerland)
|May 27, 2026
PubMed
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This summary is machine-generated.

This survey provides a decision-guided framework for selecting deep learning models in medical image segmentation. It links clinical needs to appropriate strategies, aiding in the choice of segmentation methods for diverse imaging tasks.

Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Image Analysis
  • Deep Learning for Healthcare

Background:

  • Medical image segmentation employs diverse deep learning models like CNNs, transformers, VLMs, SAM, and diffusion models.
  • While effective across MRI, CT, PET, ultrasound, and endoscopy, rapid architectural growth complicates optimal model selection for clinical constraints.
  • Existing surveys lack decision-oriented guidance for choosing segmentation methods.

Purpose of the Study:

  • To present a comprehensive, decision-guided survey of medical image segmentation paradigms.
  • To develop a practical framework for selecting segmentation strategies based on clinical scenarios and data constraints.
  • To critically examine robustness, generalization, annotation variability, and reproducibility in medical AI.

Main Methods:

Keywords:
Segment Anything Model (SAM)diffusion modelsdomain shiftmedical image segmentationmodel selectionvision-language models

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  • Systematic analysis of segmentation paradigms across imaging modalities, task types, dataset characteristics, and evaluation protocols.
  • Development of a model selection framework linking clinical scenarios (e.g., small lesion detection, 3D segmentation, limited data) to segmentation strategies.
  • Critical examination of robustness, generalization, annotation variability, and benchmarking reproducibility.

Main Results:

  • A taxonomy of deep learning segmentation methods for medical imaging.
  • A practical framework guiding model selection for specific clinical applications and data conditions.
  • An assessment of key challenges including robustness, generalization, and reproducibility.

Conclusions:

  • This work offers a clinically oriented roadmap for selecting medical image segmentation methods.
  • The proposed framework aids in navigating methodological uncertainty and optimizing model choice.
  • Highlights future research directions for reliable and reproducible medical AI systems.