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Improving Data-Efficiency and Robustness of Medical Imaging Segmentation Using Inpainting-Based Self-Supervised

Jeffrey Dominic1, Nandita Bhaskhar2, Arjun D Desai1,2

  • 1Department of Radiology, Stanford University, Stanford, CA 94305, USA.

Bioengineering (Basel, Switzerland)
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

Self-supervised learning (SSL) with context restoration pretraining improves medical image segmentation accuracy, especially in label-limited settings. This approach enhances model robustness and reduces errors compared to traditional supervised learning.

Keywords:
CTMRIdeep learningmachine learningsegmentationself-supervised learning

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Area of Science:

  • Medical imaging analysis
  • Machine learning in healthcare
  • Computer vision for diagnostics

Background:

  • Self-supervised learning (SSL) offers a promising avenue for medical image segmentation, particularly when labeled data is scarce.
  • Inpainting-based pretext tasks are effective for training models without manual annotations.
  • Evaluating different SSL training methodologies is crucial for optimizing performance in clinical applications.

Purpose of the Study:

  • To systematically evaluate the efficacy of inpainting-based pretext tasks (context prediction and context restoration) for medical image segmentation using SSL.
  • To determine the impact of various design choices on the performance of self-supervised U-Net models.
  • To compare the performance of optimized SSL models against baseline supervised models in label-limited scenarios.

Main Methods:

  • Trained multiple self-supervised U-Net models on MRI and CT datasets using different combinations of design choices and pretext tasks.
  • Identified optimal design choices, including context restoration with 32x32 patches and Poisson-disc sampling, transferring only encoder weights, and fine-tuning with a 1e-3 learning rate.
  • Compared SSL models with baseline supervised models on clinically-relevant metrics in label-limited conditions.

Main Results:

  • SSL pretraining with context restoration, specific patch settings, encoder weight transfer, and fine-tuning parameters significantly improved MRI and CT tissue segmentation accuracy (p < 0.001) over supervised learning.
  • Increased unlabeled pretraining data size consistently enhanced segmentation performance across datasets and label-limited scenarios.
  • SSL models outperformed supervised models in clinically-relevant metrics, particularly when supervised learning performance was low.

Conclusions:

  • SSL pretraining using inpainting-based pretext tasks enhances the robustness of medical image segmentation models in label-limited scenarios.
  • This approach effectively reduces worst-case errors often encountered with purely supervised learning methods.
  • The findings highlight the potential of SSL to improve diagnostic accuracy and reliability in resource-constrained medical imaging environments.