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Semi-supervised Segmentation Network Based on Prototype-Oriented Local Contrastive Learning for Pregnancy Tissue in

Ping Lou1, Jie Ying2, Feng Gao3

  • 1School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China.

Journal of Imaging Informatics in Medicine
|April 29, 2026
PubMed
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This summary is machine-generated.

This study introduces a new semi-supervised learning method for segmenting cesarean scar pregnancy (CSP) tissue. The approach improves accuracy with limited data, aiding early diagnosis and treatment of this severe ectopic pregnancy complication.

Area of Science:

  • Medical Imaging Informatics
  • Artificial Intelligence in Healthcare
  • Computational Pathology

Background:

  • Cesarean scar pregnancy (CSP) is a critical ectopic pregnancy subtype requiring early detection to prevent severe bleeding.
  • Accurate segmentation of pregnancy tissue is vital for clinical assessment but faces challenges due to morphology and data limitations.
  • Existing segmentation methods struggle with accuracy and require extensive manual annotation, which is time-consuming and costly.

Purpose of the Study:

  • To develop a semi-supervised learning framework for accurate pregnancy tissue segmentation in medical images.
  • To address the challenges of limited labeled data and fine-grained feature extraction in medical image segmentation.
  • To provide a reliable tool for clinical decision-making in early ectopic pregnancy management.
Keywords:
Local contrastive learningPregnant tissueSemi-supervised learning

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Main Methods:

  • Proposed a prototype-oriented local contrastive learning framework for semi-supervised segmentation.
  • Extracted representative prototypes to characterize feature distributions.
  • Introduced a prototype-guided local contrastive strategy to align unlabeled data with supervised centers, enhancing segmentation accuracy.

Main Results:

  • Achieved a Dice coefficient of 86.91% for CSP tissue segmentation with only a 50% labeling rate on a self-constructed dataset.
  • Demonstrated generalizability by achieving a Dice coefficient of 87.34% on a public cardiac dataset.
  • The method effectively addresses informatics challenges in medical image segmentation.

Conclusions:

  • The developed framework significantly advances semi-supervised learning for medical imaging informatics.
  • The prototype-oriented local contrastive learning method offers a reliable tool for accurate CSP tissue segmentation.
  • This approach supports improved clinical decision-making for early ectopic pregnancy management.