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Multi-agent medical image segmentation: A survey.

Mohamed T Bennai1, Zahia Guessoum2, Smaine Mazouzi3

  • 1LIMOSE Laboratory, Faculty of Sciences, University of M'hamed Bougara of Boumerdes, Avenue de l'indépendance, Boumerdes, 35000, Algeria; Université de Reims Champagne Ardenne, CReSTIC EA 3804, Reims 51097, France.

Computer Methods and Programs in Biomedicine
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Summary
This summary is machine-generated.

This study compares multi-agent systems (MAS) for automating medical image segmentation. AI-driven MAS show promise in improving the speed and accuracy of analyzing medical images for disease detection.

Keywords:
Image segmentationMedical imagesMulti-agent systemsReviewSurvey

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Computational pathology

Background:

  • Healthcare increasingly uses medical imaging for diagnosing numerous pathologies.
  • Manual image processing by radiologists is time-consuming and subjective.
  • Image segmentation is a complex task crucial for identifying tissues and organs.

Purpose of the Study:

  • To present a comparative analysis of multi-agent approaches for medical image segmentation.
  • To review recent literature on artificial intelligence-based segmentation techniques.
  • To highlight the potential of Multi-Agent Systems (MAS) in automating medical image analysis.

Main Methods:

  • Review of recently published literature on multi-agent systems for medical image segmentation.
  • Comparative analysis of different multi-agent strategies.
  • Focus on artificial intelligence techniques applied to image segmentation.

Main Results:

  • Artificial intelligence techniques, particularly Multi-Agent Systems (MAS), offer promising automation for medical image segmentation.
  • MAS paradigms are emerging as effective tools for improving segmentation accuracy and efficiency.
  • The study identifies and compares various multi-agent approaches in recent literature.

Conclusions:

  • Multi-Agent Systems (MAS) represent a significant advancement in automating medical image segmentation.
  • AI-driven segmentation techniques have the potential to overcome limitations of manual analysis.
  • Further research into MAS for medical imaging is warranted to enhance diagnostic capabilities.