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Synth-by-Reg (SbR): Contrastive Learning for Synthesis-Based Registration of Paired Images

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Simulation and Synthesis in Medical Imaging (SASHIMI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12965))

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Abstract

Nonlinear inter-modality registration is often challenging due to the lack of objective functions that are good proxies for alignment. Here we propose a synthesis-by-registration method to convert this problem into an easier intra-modality task. We introduce a registration loss for weakly supervised image translation between domains that does not require perfectly aligned training data. This loss capitalises on a registration U-Net with frozen weights, to drive a synthesis CNN towards the desired translation. We complement this loss with a structure preserving constraint based on contrastive learning, which prevents blurring and content shifts due to overfitting. We apply this method to the registration of histological sections to MRI slices, a key step in 3D histology reconstruction. Results on two public datasets show improvements over registration based on mutual information (13% reduction in landmark error) and synthesis-based algorithms such as CycleGAN (11% reduction), and are comparable to registration with label supervision. Code and data are publicly available at https://github.com/acasamitjana/SynthByReg.

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Author information

Authors and Affiliations

  1. Center for Medical Image Computing, University College London, London, UK

    Adrià Casamitjana & Juan Eugenio Iglesias

  2. Department of Neuroscience, University of Sussex, Brighton, UK

    Matteo Mancini

  3. NeuroPoly Lab, Polytechnique Montreal, Montreal, Canada

    Matteo Mancini

  4. UBRIC, Cardiff University, Cardiff, UK

    Matteo Mancini

  5. Martinos Center for Biomedical Imaging, MGH and Harvard Medical School, Boston, USA

    Juan Eugenio Iglesias

  6. Computer Science and AI Laboratory, Massachusetts Institute of Technology, Cambridge, USA

    Juan Eugenio Iglesias

Authors
  1. Adrià Casamitjana
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  2. Matteo Mancini
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  3. Juan Eugenio Iglesias
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Corresponding author

Correspondence to Adrià Casamitjana .

Editor information

Editors and Affiliations

  1. Masaryk University, Brno, Czech Republic

    David Svoboda

  2. CNRS – Paris Brain Institute, Paris, France

    Ninon Burgos

  3. University of Twente, Enschede, The Netherlands

    Jelmer M. Wolterink

  4. NVIDIA, Santa Clara, CA, USA

    Can Zhao

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Casamitjana, A., Mancini, M., Iglesias, J.E. (2021). Synth-by-Reg (SbR): Contrastive Learning for Synthesis-Based Registration of Paired Images. In: Svoboda, D., Burgos, N., Wolterink, J.M., Zhao, C. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2021. Lecture Notes in Computer Science(), vol 12965. Springer, Cham. https://doi.org/10.1007/978-3-030-87592-3_5

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  • DOI: https://doi.org/10.1007/978-3-030-87592-3_5

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-87592-3

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