Data-Agnostic Augmentations for Unknown Variations: Out-of-Distribution Generalisation in MRI Segmentation
Journal:
arXiv
Published Date:
May 15, 2025
Abstract
Medical image segmentation models are often trained on curated datasets,
leading to performance degradation when deployed in real-world clinical
settings due to mismatches between training and test distributions. While data
augmentation techniques are widely used to address these challenges,
traditional visually consistent augmentation strategies lack the robustness
needed for diverse real-world scenarios. In this work, we systematically
evaluate alternative augmentation strategies, focusing on MixUp and Auxiliary
Fourier Augmentation. These methods mitigate the effects of multiple variations
without explicitly targeting specific sources of distribution shifts. We
demonstrate how these techniques significantly improve out-of-distribution
generalization and robustness to imaging variations across a wide range of
transformations in cardiac cine MRI and prostate MRI segmentation. We
quantitatively find that these augmentation methods enhance learned feature
representations by promoting separability and compactness. Additionally, we
highlight how their integration into nnU-Net training pipelines provides an
easy-to-implement, effective solution for enhancing the reliability of medical
segmentation models in real-world applications.
