Skip to main content

Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation - Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution

  • Conference paper
  • First Online:
Predictive Intelligence in Medicine (PRIME 2021)

Abstract

Predicting disease progression always involves a high degree of uncertainty. White matter hyperintensities (WMHs) are the main neuroradiological feature of small vessel disease and a common finding in brain scans of dementia patients and older adults. In predicting their progression previous studies have identified two main challenges: 1) uncertainty in predicting the areas/boundaries of shrinking and growing WMHs and 2) uncertainty in the estimation of future WMHs volume. This study proposes the use of a probabilistic deep learning model called Probabilistic U-Net trained with adversarial loss for capturing and modelling spatial uncertainty in brain MR images. This study also proposes an evaluation procedure named volume interval estimation (VIE) for improving the interpretation of and confidence in the predictive deep learning model. Our experiments show that the Probabilistic U-Net with adversarial training improved the performance of non-probabilistic U-Net in Dice similarity coefficient for predicting the areas of shrinking WMHs, growing WMHs, stable WMHs, and their average by up to 3.35%, 2.94%, 0.47%, and 1.03% respectively. It also improved the volume estimation by 11.84% in the “Correct Prediction in Estimated Volume Interval” metric as per the newly proposed VIE evaluation procedure.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Castorina, L.V., et al.: Metrics for quality control of results from super-resolution machine-learning algorithms-data extracted from publications in the period 2017- May 2021 [dataset] (2021). https://doi.org/10.7488/ds/3062

  2. Chappell, F.M., et al.: Sample size considerations for trials using cerebral white matter hyperintensity progression as an intermediate outcome at 1 year after mild stroke: results of a prospective cohort study. Trials 18(1), 1–10 (2017). https://doi.org/10.1186/s13063-017-1825-7

    Article  Google Scholar 

  3. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)

    Google Scholar 

  4. Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. Mach. Learn. 110(3), 457–506 (2021). https://doi.org/10.1007/s10994-021-05946-3

    Article  MathSciNet  Google Scholar 

  5. Kohl, S., et al.: A probabilistic U-net for segmentation of ambiguous images. In: Advances in Neural Information Processing Systems, vol. 31 (2018)

    Google Scholar 

  6. Lin, T.Y., et al.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017). https://doi.org/10.1109/ICCV.2017.324

  7. Miyato, T., et al.: Spectral normalization for generative adversarial networks. In: International Conference on Learning Representations (2018)

    Google Scholar 

  8. Rachmadi, M.F., et al.: Automatic spatial estimation of white matter hyperintensities evolution in brain MRI using disease evolution predictor deep neural networks. Med. Image Anal. 63, 101712 (2020). https://doi.org/10.1016/j.media.2020.101712

    Article  Google Scholar 

  9. Rachmadi, M.F., del C. Valdés-Hernández, M., Makin, S., Wardlaw, J.M., Komura, T.: Predicting the evolution of white matter hyperintensities in brain MRI using generative adversarial networks and irregularity map. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 146–154. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32248-9_17

    Chapter  Google Scholar 

  10. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  11. Valdés Hernández, M.D.C., et al.: Rationale, design and methodology of the image analysis protocol for studies of patients with cerebral small vessel disease and mild stroke. Brain Behav. 5(12), e00415 (2015). https://doi.org/10.1002/brb3.415

  12. Wardlaw, J.M., et al.: White matter hyperintensity reduction and outcomes after minor stroke. Neurology 89(10), 1003–1010 (2017). https://doi.org/10.1212/WNL.0000000000004328

    Article  Google Scholar 

Download references

Acknowledgements

Funds from JSPS (Kakenhi Grant-in-Aid for Research Activity Start-up, Project No. 20K23356) (MFR); Row Fogo Charitable Trust (Grant No. BRO-D.FID3668413) (MCVH); Wellcome Trust (patient recruitment, scanning, primary study Ref No. WT088134/Z/09/A); Fondation Leducq (Perivascular Spaces Transatlantic Network of Excellence); EU Horizon 2020 (SVDs@Target); and the MRC UK Dementia Research Institute at the University of Edinburgh (Wardlaw programme) are gratefully acknowledged. This research was also supported by the program for Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from the Japan Agency for Medical Research and Development AMED (JP21dm0207001).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Muhammad Febrian Rachmadi .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 572 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rachmadi, M.F., Valdés-Hernández, M.d.C., Maulana, R., Wardlaw, J., Makin, S., Skibbe, H. (2021). Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation - Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution. In: Rekik, I., Adeli, E., Park, S.H., Schnabel, J. (eds) Predictive Intelligence in Medicine. PRIME 2021. Lecture Notes in Computer Science(), vol 12928. Springer, Cham. https://doi.org/10.1007/978-3-030-87602-9_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-87602-9_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87601-2

  • Online ISBN: 978-3-030-87602-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics