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Pyramid feature adaptation for semi-supervised cardiac bi-ventricle segmentation.

Chengjin Yu1, Yuanting Yan1, Shu Zhao1

  • 1Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|February 23, 2020
PubMed
Summary

This study introduces a novel semi-supervised method for cardiac bi-ventricle segmentation (BVS), improving accuracy with limited labeled data. The pyramid feature adaptation approach enhances cardiac imaging analysis for clinical applications.

Keywords:
Cardiac bi-ventricle segmentationPyramid feature adaptationSemi-supervised learning

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Area of Science:

  • Medical Imaging Analysis
  • Cardiovascular Research
  • Artificial Intelligence in Medicine

Background:

  • Cardiac bi-ventricle segmentation (BVS) is crucial for evaluating cardiac function (e.g., ejection fraction).
  • BVS is challenging due to structural variability and limited labeled data in medical datasets.
  • Accurate BVS is essential for reliable assessment of left ventricle (LV) and right ventricle (RV) volumes.

Purpose of the Study:

  • To develop an effective semi-supervised method for cardiac bi-ventricle segmentation (BVS).
  • To address the challenges of high structural variability and data scarcity in BVS.
  • To improve the accuracy and clinical applicability of cardiac segmentation techniques.

Main Methods:

  • Proposed a pyramid feature adaptation based semi-supervised method (PABVS) for BVS.
  • Extracted multiscale pyramid features to handle bi-ventricle structure variability.
  • Employed a weighted pyramid feature adaptation strategy using adversarial learning for smooth feature spaces between labeled and unlabeled data.

Main Results:

  • Achieved a Dice score of 0.915 for EpiLV segmentation using only 40% labeled data.
  • Reached a Dice score of 0.976 for EpiLV segmentation with fully labeled data.
  • Outperformed existing mainstream semi-supervised methods in cardiac bi-ventricle segmentation.

Conclusions:

  • The proposed PABVS method demonstrates high efficacy in cardiac bi-ventricle segmentation, even with limited labeled data.
  • PABVS effectively handles structural variability through multiscale feature extraction and adaptation.
  • The method shows significant potential for practical clinical applications in cardiac imaging.