Medical image synthesis for data augmentation and anonymization using generative adversarial networks

Hoo Chang Shin, Neil A. Tenenholtz, Jameson K. Rogers, Christopher Schwarz, Matthew L. Senjem, Jeffrey L. Gunter, Katherine P. Andriole, Mark Michalski

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

59 Scopus citations

Abstract

Data diversity is critical to success when training deep learning models. Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models. In this work, we propose a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network using two publicly available data sets of brain MRI. We demonstrate two unique benefits that the synthetic images provide. First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images as a form of data augmentation. Second, we demonstrate the value of generative models as an anonymization tool, achieving comparable tumor segmentation results when trained on the synthetic data versus when trained on real subject data. Together, these results offer a potential solution to two of the largest challenges facing machine learning in medical imaging, namely the small incidence of pathological findings, and the restrictions around sharing of patient data.

Original languageEnglish (US)
Title of host publicationSimulation and Synthesis in Medical Imaging - Third International Workshop, SASHIMI 2018, Held in Conjunction with MICCAI 2018, Proceedings
EditorsOrcun Goksel, Ipek Oguz, Ali Gooya, Ninon Burgos
PublisherSpringer Verlag
Pages1-11
Number of pages11
ISBN (Print)9783030005351
DOIs
StatePublished - Jan 1 2018
Event3rd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2018 Held in Conjunction with 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: Sep 16 2018Sep 16 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11037 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2018 Held in Conjunction with 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
CountrySpain
CityGranada
Period9/16/189/16/18

Keywords

  • Brain tumor
  • Deep learning
  • GAN
  • Generative models
  • Image synthesis
  • Magnetic resonance imaging
  • MRI
  • Segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Shin, H. C., Tenenholtz, N. A., Rogers, J. K., Schwarz, C., Senjem, M. L., Gunter, J. L., Andriole, K. P., & Michalski, M. (2018). Medical image synthesis for data augmentation and anonymization using generative adversarial networks. In O. Goksel, I. Oguz, A. Gooya, & N. Burgos (Eds.), Simulation and Synthesis in Medical Imaging - Third International Workshop, SASHIMI 2018, Held in Conjunction with MICCAI 2018, Proceedings (pp. 1-11). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11037 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00536-8_1