An augmentation strategy to mimic multi-scanner variability in MRI
This addresses a critical issue for medical imaging researchers and practitioners by enhancing model robustness in real-world multi-center settings, though it is incremental as it builds on existing augmentation and deep learning methods.
The paper tackles the problem of poor generalization of brain MRI models from homogeneous single-scanner datasets to multi-scanner clinical data by proposing a novel data augmentation strategy, resulting in improved model generalization to unseen scanners.
Most publicly available brain MRI datasets are very homogeneous in terms of scanner and protocols, and it is difficult for models that learn from such data to generalize to multi-center and multi-scanner data. We propose a novel data augmentation approach with the aim of approximating the variability in terms of intensities and contrasts present in real world clinical data. We use a Gaussian Mixture Model based approach to change tissue intensities individually, producing new contrasts while preserving anatomical information. We train a deep learning model on a single scanner dataset and evaluate it on a multi-center and multi-scanner dataset. The proposed approach improves the generalization capability of the model to other scanners not present in the training data.