Multi-Source Domain Adaptation for Medical Image Segmentation.

Area of Science:

• Medical Image Analysis
• Computer Vision
• Machine Learning

Background:

• Unsupervised domain adaptation (UDA) addresses performance degradation in models due to domain shifts.
• Current UDA segmentation methods primarily focus on single-source scenarios.
• Practical applications often involve multiple labeled source domains, offering richer knowledge for transfer.

Purpose of the Study:

• To investigate and develop a framework for multi-source unsupervised domain adaptation in medical image segmentation.
• To leverage knowledge from multiple source domains for improved target domain adaptation.
• To enhance the performance of medical image segmentation models in cross-domain scenarios.

Main Methods:

• A multi-level adversarial learning scheme is employed to adapt features across different levels between source and target domains.
• A multi-model consistency loss is proposed to simultaneously transfer knowledge from multiple sources to the target domain.
• The framework is validated on cardiac and liver segmentation tasks.

Main Results:

• The proposed framework demonstrates promising performance in medical image segmentation.
• The method achieves favorable comparisons against existing state-of-the-art approaches.
• Effective knowledge transfer from multiple sources enhances segmentation accuracy.

Conclusions:

• The developed framework successfully addresses the challenge of multi-source UDA for medical image segmentation.
• The integration of multi-level adversarial learning and multi-model consistency loss improves cross-domain adaptation.
• This approach offers a significant advancement for medical image segmentation in diverse data scenarios.