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Positron Emission Tomography01:29

Positron Emission Tomography

Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
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Related Experiment Video

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SAM-driven cross prompting with adaptive sampling consistency for semi-supervised medical image segmentation.

Juzheng Miao1, Cheng Chen2, Yuchen Yuan1

  • 1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.

Medical Image Analysis
|February 20, 2026
PubMed
Summary

This study introduces CPAC-SAM, a new semi-supervised learning method for medical image segmentation that leverages the Segment Anything Model (SAM). It significantly improves segmentation accuracy by effectively using limited labeled and abundant unlabeled data.

Keywords:
Adaptive grid samplingPrompt consistencySegment anything modelSemi-supervised segmentation

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computer Vision

Background:

  • Semi-supervised learning (SSL) is crucial for medical image segmentation due to limited labeled data.
  • Visual foundation models like Segment Anything Model (SAM) offer improved sample efficiency.
  • Harnessing foundation models for SSL in medical imaging remains a challenge.

Purpose of the Study:

  • To propose a novel SAM-driven framework (CPAC-SAM) for semi-supervised medical image segmentation.
  • To enhance learning from both labeled and unlabeled data using cross prompting and adaptive sampling.
  • To improve prompt reliability and consistency for robust segmentation.

Main Methods:

  • Developed a SAM-driven cross prompting framework with a dual-branch structure.
  • Implemented a prototype-guided grid sampling strategy for adaptive prompt generation.
  • Introduced prompt consistency regularization to reduce SAM's sensitivity.

Main Results:

  • CPAC-SAM demonstrated superior performance over state-of-the-art SSL methods across five medical image segmentation tasks (2D and 3D).
  • Achieved significant Dice improvements, including over 4.1% for breast cancer and 3.8% for left atrium segmentation.
  • Validated effectiveness across various labeled-data ratios and modalities.

Conclusions:

  • CPAC-SAM effectively integrates foundation models into semi-supervised medical image segmentation.
  • The proposed cross prompting, adaptive sampling, and consistency regularization enhance learning efficiency and accuracy.
  • This framework offers a promising approach for advancing medical image analysis with limited labeled data.