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Semi-supervised task-driven data augmentation for medical image segmentation.

Krishna Chaitanya1, Neerav Karani1, Christian F Baumgartner1

  • 1Computer Vision Laboratory, ETH Zurich, Sternwartstrasse 7, Zurich- 8092, Switzerland.

Medical Image Analysis
|January 1, 2021
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel task-driven data augmentation method to improve medical image segmentation with limited labeled data. The approach optimizes synthetic data generation for better accuracy compared to standard augmentation techniques.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Machine Learning

Background:

  • Supervised learning for medical image segmentation requires extensive annotated data, which is costly and time-consuming to acquire.
  • Existing methods for limited annotation in medical imaging have not significantly improved segmentation accuracy over basic data augmentation.

Purpose of the Study:

  • To develop a novel task-driven data augmentation method for optimizing medical image segmentation with limited labeled data.
  • To improve the generalization and accuracy of segmentation models when training data is scarce.

Main Methods:

  • Proposed a task-driven data augmentation technique where a synthetic data generator is optimized for the segmentation task.
  • Modeled intensity and shape variations using additive intensity transformations and deformation fields.
Keywords:
Data augmentationDeep learningMachine learningMedical image segmentationSemi-supervised learning

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  • Optimized transformations within a semi-supervised framework using both labeled and unlabeled data.

    • Main Results:

    • The proposed method significantly outperformed standard augmentation and other semi-supervised approaches in limited annotation settings.
    • Experiments demonstrated superior performance on cardiac, prostate, and pancreas medical imaging datasets.
    • Achieved higher accuracy in medical image segmentation with scarce annotated samples.

    Conclusions:

    • Task-driven data augmentation offers a significant advancement for medical image segmentation with limited annotations.
    • The developed semi-supervised framework effectively leverages unlabeled data to enhance segmentation performance.
    • This approach provides a viable solution for reducing the dependency on large annotated medical datasets.