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

Maria Ines Meyer*, Ezequiel de la Rosa, Nuno Pedrosa de Barros, Roberto Paolella, Koen Van Leemput, Diana M. Sima

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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.

Original languageEnglish
Article number708196
JournalFrontiers in Neuroscience
Volume15
Number of pages15
ISSN1662-4548
DOIs
Publication statusPublished - 2021

Bibliographical note

Funding Information:
This work was supported by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreements nos 765148 and 764513, by the NIH NINDS grant no R01NS112161 and by the Penta project 19021 (Vivaldy).

Funding Information:
This work builds on the preliminary research paper An augmentation strategy to mimic multi-scanner variability in MRI, accepted for presentation at the International Symposium on Biomedical Imaging (ISBI) 2021 and to be published in conference proceedings (Meyer et al., 2021). Funding. This work was supported by the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreements nos 765148 and 764513, by the NIH NINDS grant no R01NS112161 and by the Penta project 19021 (Vivaldy).

Keywords

  • Data augmentation
  • Gaussian mixture models
  • Magnetic resonance imaging
  • Multi-scanner
  • Segmentation

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