Training data independent image registration with gans using transfer learning and segmentation information

Dwarikanath Mahapatra, Zongyuan Ge

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

10 Citations (Scopus)


Registration is an important task in automated medical image analysis. Although deep learning (DL) based image registration methods out perform time consuming conventional approaches, they are heavily dependent on training data and do not generalize well for new images types. We present a DL based approach that can register an image pair which is different from the training images. This is achieved by training generative adversarial networks (GANs) in combination with segmentation information and transfer learning. Experiments on chest Xray and brain MR images show that our method gives better registration performance over conventional methods.

Original languageEnglish
Title of host publicationProceedings of 2019 IEEE International Symposium on Biomedical Imaging
EditorsErik Meijering, Marius George Linguraru
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781538636411
ISBN (Print)9781538636404
Publication statusPublished - 2019
EventIEEE International Symposium on Biomedical Imaging (ISBI) 2019 - Hilton Molino Stucky, Venice, Italy
Duration: 8 Apr 201911 Apr 2019
Conference number: 16th (Proceedings)

Publication series

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


ConferenceIEEE International Symposium on Biomedical Imaging (ISBI) 2019
Abbreviated titleISBI 2019
Internet address


  • Gans
  • Mri
  • Registration
  • Segmentation
  • Transfer learning
  • Xray

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