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A Contrast Augmentation Approach to Improve Multi-Scanner Generalization in MRI
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 a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8439197/ https://www.ncbi.nlm.nih.gov/pubmed/34531715 http://dx.doi.org/10.3389/fnins.2021.708196 |
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author | Meyer, Maria Ines de la Rosa, Ezequiel Pedrosa de Barros, Nuno Paolella, Roberto Van Leemput, Koen Sima, Diana M. |
author_facet | Meyer, Maria Ines de la Rosa, Ezequiel Pedrosa de Barros, Nuno Paolella, Roberto Van Leemput, Koen Sima, Diana M. |
author_sort | Meyer, Maria Ines |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8439197 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84391972021-09-15 A Contrast Augmentation Approach to Improve Multi-Scanner Generalization in MRI Meyer, Maria Ines de la Rosa, Ezequiel Pedrosa de Barros, Nuno Paolella, Roberto Van Leemput, Koen Sima, Diana M. Front Neurosci Neuroscience 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. Frontiers Media S.A. 2021-08-31 /pmc/articles/PMC8439197/ /pubmed/34531715 http://dx.doi.org/10.3389/fnins.2021.708196 Text en Copyright © 2021 Meyer, de la Rosa, Pedrosa de Barros, Paolella, Van Leemput and Sima. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Meyer, Maria Ines de la Rosa, Ezequiel Pedrosa de Barros, Nuno Paolella, Roberto Van Leemput, Koen Sima, Diana M. A Contrast Augmentation Approach to Improve Multi-Scanner Generalization in MRI |
title | A Contrast Augmentation Approach to Improve Multi-Scanner Generalization in MRI |
title_full | A Contrast Augmentation Approach to Improve Multi-Scanner Generalization in MRI |
title_fullStr | A Contrast Augmentation Approach to Improve Multi-Scanner Generalization in MRI |
title_full_unstemmed | A Contrast Augmentation Approach to Improve Multi-Scanner Generalization in MRI |
title_short | A Contrast Augmentation Approach to Improve Multi-Scanner Generalization in MRI |
title_sort | contrast augmentation approach to improve multi-scanner generalization in mri |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8439197/ https://www.ncbi.nlm.nih.gov/pubmed/34531715 http://dx.doi.org/10.3389/fnins.2021.708196 |
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