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A Contrast Augmentation Approach to Improve Multi-Scanner Generalization in MRI

Authors :
Maria Ines Meyer
Ezequiel de la Rosa
Nuno Pedrosa de Barros
Roberto Paolella
Koen Van Leemput
Diana M. Sima
Source :
Frontiers in Neuroscience, Vol 15 (2021)
Publication Year :
2021
Publisher :
Frontiers Media S.A., 2021.

Abstract

Most data-driven methods are very susceptible to data variability. This problem is particularly apparent when applying Deep Learning (DL) to brain Magnetic Resonance Imaging (MRI), where intensities and contrasts vary due to acquisition protocol, scanner- and center-specific factors. Most publicly available brain MRI datasets originate from the same center and are homogeneous in terms of scanner and used protocol. As such, devising robust methods that generalize to multi-scanner and multi-center data is crucial for transferring these techniques into clinical practice. We propose a novel data augmentation approach based on Gaussian Mixture Models (GMM-DA) with the goal of increasing the variability of a given dataset in terms of intensities and contrasts. The approach allows to augment the training dataset such that the variability in the training set compares to what is seen in real world clinical data, while preserving anatomical information. We compare the performance of a state-of-the-art U-Net model trained for segmenting brain structures with and without the addition of GMM-DA. The models are trained and evaluated on single- and multi-scanner datasets. Additionally, we verify the consistency of test-retest results on same-patient images (same and different scanners). Finally, we investigate how the presence of bias field influences the performance of a model trained with GMM-DA. We found that the addition of the GMM-DA improves the generalization capability of the DL model to other scanners not present in the training data, even when the train set is already multi-scanner. Besides, the consistency between same-patient segmentation predictions is improved, both for same-scanner and different-scanner repetitions. We conclude that GMM-DA could increase the transferability of DL models into clinical scenarios.

Details

Language :
English
ISSN :
1662453X
Volume :
15
Database :
Directory of Open Access Journals
Journal :
Frontiers in Neuroscience
Publication Type :
Academic Journal
Accession number :
edsdoj.5f0d2a26ae5f4c04804acaa7faed7b04
Document Type :
article
Full Text :
https://doi.org/10.3389/fnins.2021.708196