An Augmentation Strategy to Mimic Multi-Scanner Variability in MRI

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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.
Original languageEnglish
Title of host publicationProceedings of the 2021 IEEE 18th International Symposium on Biomedical Imaging
PublisherIEEE
Publication date2021
Pages1196-1200
ISBN (Electronic)978-1-6654-1246-9
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Symposium on Biomedical Imaging - Virtual, Nice, France
Duration: 13 Apr 202116 Apr 2021
Conference number: 18
https://biomedicalimaging.org/2021/

Conference

Conference2021 IEEE International Symposium on Biomedical Imaging
Number18
LocationVirtual
Country/TerritoryFrance
CityNice
Period13/04/202116/04/2021
Internet address

Fingerprint

Dive into the research topics of 'An Augmentation Strategy to Mimic Multi-Scanner Variability in MRI'. Together they form a unique fingerprint.

Cite this