Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging

TL;DR

This paper introduces an unsupervised domain adaptation method for medical imaging that uses MRI-specific augmentation techniques to improve brain MRI segmentation across diverse datasets and conditions.

Contribution

It presents a novel MRI-specific augmentation approach for unsupervised domain adaptation, enhancing robustness and accuracy in brain MRI segmentation tasks.

Findings

Achieves superior accuracy over state-of-the-art methods.

Demonstrates broad applicability across datasets and modalities.

Shows robustness against domain shift in multiple segmentation tasks.

Abstract

Deep learning-based models in medical imaging often struggle to generalize effectively to new scans due to data heterogeneity arising from differences in hardware, acquisition parameters, population, and artifacts. This limitation presents a significant challenge in adopting machine learning models for clinical practice. We propose an unsupervised method for robust domain adaptation in brain MRI segmentation by leveraging MRI-specific augmentation techniques. To evaluate the effectiveness of our method, we conduct extensive experiments across diverse datasets, modalities, and segmentation tasks, comparing against the state-of-the-art methods. The results show that our proposed approach achieves high accuracy, exhibits broad applicability, and showcases remarkable robustness against domain shift in various tasks, surpassing the state-of-the-art performance in the majority of cases.