Deep Semi-Supervised Bias Field Correction Of Mr Images

Tal Goldfryd, Shiri Gordon, Tamar Riklin Raviv

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

A bias field is an artifact inherent to MRI scanners which is manifested by a smooth intensity variation across the scans. We present an innovative generative approach to address the inverse problem of bias field estimation and removal in a semi-supervised manner. The key contribution is the construction of a compound framework of four interacting, adversarial neural networks. Specifically, we simultaneously train a pair of neural networks, one for the reconstruction of the plain bias field and the other for the reconstruction of a bias-free MRI scan, such that the output of each together with the input biased scans define the loss of the other network. A third network, trained as a bias-field discriminator provides an additional loss to the bias field generator while an MRI segmentation network provides an additional loss to the bias-free MRI generator. We trained and validated our framework using real MRI scans with simulated bias fields and tested it on publicly available brain data-sets as well as private data yielding results competitive with state-of-the-art methods. Code is available upon request.
Original languageEnglish GB
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
PublisherIEEE Computer Society
Pages1836-1840
Number of pages5
ISBN (Electronic)9781665412469
DOIs
StatePublished - 2021
Event18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France
Duration: 13 Apr 202116 Apr 2021

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2021-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
Country/TerritoryFrance
CityNice
Period13/04/2116/04/21

Keywords

  • Adversarial Neural Networks
  • Bias field correction
  • Brain MRI
  • Deep Learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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