Enhancing MR image segmentation with realistic adversarial data augmentation.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Computer Vision

Background:

• Neural networks for medical image segmentation require large labeled datasets, which are costly and difficult to obtain due to privacy concerns.
• Existing data augmentation methods may not sufficiently capture realistic imaging variations crucial for robust model training.

Purpose of the Study:

• To introduce AdvChain, a novel adversarial data augmentation framework designed to improve the diversity and effectiveness of training data for medical image segmentation.
• To address the challenges of limited labeled data in medical imaging by enhancing network generalizability.

Main Methods:

• AdvChain employs dynamic data augmentation, creating chained photometric and geometric transformations to simulate realistic imaging variations.
• The framework jointly optimizes the data augmentation model and a segmentation network, generating challenging examples to boost generalization.
• It functions as a plug-in module, independent of generative networks, and is computationally efficient.

Main Results:

• Evaluation on cardiac and prostate MRI segmentation tasks with limited labeled data demonstrated the effectiveness of AdvChain.
• The proposed method significantly alleviates the dependency on large labeled datasets.
• AdvChain improved the generalization ability of segmentation models.

Conclusions:

• AdvChain offers a practical solution for medical image segmentation by enhancing data augmentation strategies.
• The framework's ability to improve model performance with limited data highlights its value in real-world medical imaging applications.
• Its plug-in nature and computational efficiency make it broadly applicable to various segmentation networks and learning paradigms.