Skip to main content

Unlearning Scanner Bias for MRI Harmonisation

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12262)

Abstract

Combining datasets is vital for increased statistical power, especially for neurological conditions where limited data is available. However, variance due to differences in acquisition protocol and hardware limits our ability to combine datasets. We propose an iterative training scheme based on domain adaptation techniques, aiming to create scanner-invariant features while simultaneously maintaining overall performance on the main task. We demonstrate this on age prediction, but expect that our proposed training scheme will be applicable to any feedforward network and classification or regression task. We show that not only can we harmonise three MRI datasets from different studies, but can also successfully adapt the training to work with very biased datasets. The training scheme should, therefore, be applicable to most real-world data scenarios, enabling harmonisation for the task of interest.

Keywords

  • Harmonisation
  • Joint domain adaptation
  • MRI

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-59713-9_36
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-59713-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   149.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

References

  1. Alfaro-Almagro, F., et al.: Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. bioRxiv 166, 130385 (2017)

    Google Scholar 

  2. Alvi, M.S., Zisserman, A., Nellåker, C.: Turning a blind eye: explicit removal of biases and variation from deep neural network embeddings. In: ECCV Workshops (2018)

    Google Scholar 

  3. Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Vaughan, J.: A theory of learning from different domains. Mach. Learn. 79, 151–175 (2010)

    MathSciNet  CrossRef  Google Scholar 

  4. Dewey, B., et al.: DeepHarmony: a deep learning approach to contrast harmonization across scanner changes. Magn. Reson. Imaging 64, 160–170 (2019)

    CrossRef  Google Scholar 

  5. Filippini, N., et al.: Study protocol: the Whitehall II imaging sub-study. BMC psychiatry 14, 159 (2014). https://doi.org/10.1186/1471-244X-14-159

    CrossRef  Google Scholar 

  6. Fortin, J.P., et al.: Harmonization of cortical thickness measurements across scanners and sites. NeuroImage 167, 104–120 (2017). https://doi.org/10.1016/j.neuroimage.2017.11.024

    CrossRef  Google Scholar 

  7. Ganin, Y., Lempitsky, V.S.: Unsupervised domain adaptation by back propagation. ArXiv (2014)

    Google Scholar 

  8. Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17, 59:1–59:35 (2015)

    MathSciNet  MATH  Google Scholar 

  9. Hoffman, J., Tzeng, E., Darrell, T., Saenko, K.: Simultaneous deep transfer across domains and tasks. 2015 IEEE International Conference on Computer Vision (ICCV), pp. 4068–4076 (2015)

    Google Scholar 

  10. van der Maaten, L., Hinton, G.: Viualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)

    MATH  Google Scholar 

  11. Marcus, D., Wang, T., Parker, J., Csernansky, J., Morris, J., Buckner, R.: Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci. 19, 1498–507 (2007). https://doi.org/10.1162/jocn.2007.19.9.1498

  12. Pomponio, R., et al.: Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. NeuroImage 208, 116450 (2019). https://doi.org/10.1016/j.neuroimage.2019.116450

    CrossRef  Google Scholar 

  13. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition, September 2014

    Google Scholar 

  14. Sudlow, C., et al.: UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. Plos Med. 12, e1001779 (2015)

    CrossRef  Google Scholar 

  15. Wachinger, C., Rieckmann, A., Pölsterl, S.: Detect and correct bias in multi-site neuroimaging datasets. bioRxiv (2020)

    Google Scholar 

  16. Wilson, G., Cook, D.J.: A survey of unsupervised deep domain adaptation (2018)

    Google Scholar 

  17. Zhao, F., Wu, Z., Wang, L., Lin, W., Xia, S., Li, G.: Harmonization of infant cortical thickness using surface-to-surface cycle-consistent adversarial networks, pp. 475–483 (2019)

    Google Scholar 

  18. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2242–2251 (2017)

    Google Scholar 

Download references

Acknowledgements

ND is supported by the Engineering and Physical Sciences Research Council (EPSRC) and Medical Research Council (MRC) [grant number EP/L016052/1]. MJ is supported by the National Institute for Health Research (NIHR), Oxford Biomedical Research Centre (BRC), and this research was funded by the Wellcome Trust [215573/Z/19/Z]. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust [203139/Z/16/Z]. AN is grateful for support from the UK Royal Academy of Engineering under the Engineering for Development Research Fellowships scheme.The computational aspects of this research were supported by the Wellcome Trust Core Award [Grant Number 203141/Z/16/Z] and the NIHR Oxford BRC. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nicola K. Dinsdale .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Dinsdale, N.K., Jenkinson, M., Namburete, A.I.L. (2020). Unlearning Scanner Bias for MRI Harmonisation. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-59713-9_36

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59712-2

  • Online ISBN: 978-3-030-59713-9

  • eBook Packages: Computer ScienceComputer Science (R0)