An augmentation strategy to mimic multi-scanner variability in MRI

• URL: http://arxiv.org/abs/2103.12595v1
• Date: Tue, 23 Mar 2021 14:49:38 GMT
• Title: An augmentation strategy to mimic multi-scanner variability in MRI
• Authors: Maria Ines Meyer, Ezequiel de la Rosa, Nuno Barros, Roberto Paolella, Koen Van Leemput and Diana M. Sima
• Abstract summary: Most publicly available brain MRI datasets are very homogeneous in terms of scanner and protocols. 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.
• Score: 0.2839215671218125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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