Generation of synthetic structural magnetic resonance images for deep learning pre-training

Eduardo Castro, Alvaro Ulloa, Sergey M. Plis, Jessica A. Turner, Vince D. Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Deep learning methods have significantly improved classification accuracy in different areas such as speech, object and text recognition. However, this field has only began to be explored in the brain imaging field, which differs from other fields in terms of the amount of data available, its data dimensionality and other factors. This paper proposes a methodology to generate an extensive synthetic structural magnetic resonance imaging (sMRI) dataset to be used at the pre-training stage of a shallow network model to address the issue of having a limited amount of data available. Our results show that by extending our dataset using 5,000 synthetic sMRI volumes for pretraining, which accounts to approximately 10 times the size of the original dataset, we can obtain a 5% average improvement on classification results compared to the regular approach on a schizophrenia dataset. While the use of synthetic sMRI data for pre-training has only been tested on a shallow network, this can be readily applied to deeper networks.

Original languageEnglish (US)
Title of host publication2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PublisherIEEE Computer Society
Pages1057-1060
Number of pages4
ISBN (Electronic)9781479923748
DOIs
StatePublished - Jul 21 2015
Externally publishedYes
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: Apr 16 2015Apr 19 2015

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2015-July
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
CountryUnited States
CityBrooklyn
Period4/16/154/19/15

Keywords

  • deep learning
  • pretraining
  • schizophrenia
  • simulation
  • structural MRI

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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