Multi-domain Adaptation in Brain MRI Through Paired Consistency and Adversarial Learning.

Area of Science:

• Medical image analysis
• Machine learning
• Computer vision

Background:

• Supervised learning models struggle with generalization across varying medical image acquisition parameters.
• Domain adaptation (DA) methods use labeled source data to improve performance on unlabeled target data.
• Existing DA methods typically focus on adapting to a single target domain.

Purpose of the Study:

• To develop a novel multi-domain adaptation method for medical image segmentation.
• To enable adaptation from one source domain to multiple target domains using paired data.
• To improve the generalization of deep learning models in medical imaging.

Main Methods:

• A multi-domain adaptation approach combining consistency loss and adversarial learning.
• Utilizing paired data across source and multiple target domains.
• Applying the method to white matter lesion hyperintensity segmentation in brain MRIs.

Main Results:

• The proposed multi-domain adaptation method significantly improved segmentation performance.
• The method demonstrated superior results compared to existing domain adaptation baselines.
• Successful adaptation from the MICCAI 2017 challenge dataset to two distinct target domains.

Conclusions:

• The novel multi-domain adaptation technique effectively addresses the challenge of domain shift in medical imaging.
• This approach enhances model generalization across multiple target domains.
• The method shows promise for robust medical image analysis in diverse clinical settings.