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Predicting the Evolution of White Matter Hyperintensities in Brain MRI Using Generative Adversarial Networks and Irregularity Map

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11766)

Abstract

We propose a Generative Adversarial Network (GAN) model named Disease Evolution Predictor GAN (DEP-GAN) to predict the evolution (i.e., progression and regression) of White Matter Hyperintensities (WMH) in small vessel disease. In this study, the evolution of WMH is represented by the “Disease Evolution Map” (DEM) produced by subtracting irregularity map (IM) images from two time points: baseline and follow-up. DEP-GAN uses two discriminators (critics) to enforce anatomically realistic follow-up image and DEM. To simulate the non-deterministic and unknown parameters involved in WMH evolution, we propose modulating an array of random noises to the DEP-GAN’s generator which forces the model to imitate a wider spectrum of alternatives in the results. Our study shows that the use of two critics and random noises modulation in the proposed DEP-GAN improves its performance predicting the evolution of WMH in small vessel disease. DEP-GAN is able to estimate WMH volume in the follow-up year with mean (std) estimation error of −1.91 (12.12) ml and predict WMH evolution with mean rate of \(72.01\%\) accuracy (i.e., \(88.69\%\) and \(23.92\%\) better than Wasserstein GAN).

Keywords

  • Evolution of WMH
  • DEP-GAN
  • Disease progression

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Notes

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    https://github.com/baumgach/vagan-code.

References

  1. Baumgartner, C.F., et al.: Visual feature attribution using wasserstein GANs. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8309–8319, June 2018. https://doi.org/10.1109/CVPR.2018.00867

  2. Chappell, F.M., et al.: Sample size considerations for trials using cerebral white matter hyperintensity progression as an intermediate outcome at 1year after mild stroke: results of a prospective cohort study. Trials 18(1), 78 (2017). https://doi.org/10.1186/s13063-017-1825-7

    CrossRef  Google Scholar 

  3. Choi, H., et al.: Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behav. Brain Res. 344, 103–109 (2018). https://doi.org/10.1016/j.bbr.2018.02.017

    CrossRef  Google Scholar 

  4. Gulrajani, I., et al.: Improved training of wasserstein GANs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS 2017, pp. 5769–5779. Curran Associates Inc. (2017)

    Google Scholar 

  5. Jenkinson, M., et al.: Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17(2), 825–841 (2002). https://doi.org/10.1006/nimg.2002.1132

    CrossRef  Google Scholar 

  6. Perez, E., et al.: Film: visual reasoning with a general conditioning layer. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018). https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16528

  7. Rachmadi, M.F., et al.: Limited one-time sampling irregularity map (LOTS-IM) for automatic unsupervised assessment of white matter hyperintensities and multiple sclerosis lesions in structural brain magnetic resonance images. bioRxiv, 334292v5 (2019). https://doi.org/10.1101/334292

  8. Rachmadi, M.F., Valdés-Hernández, M.C., Komura, T.: Automatic irregular texture detection in brain MRI without human supervision. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 506–513. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_58

    CrossRef  Google Scholar 

  9. 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

    CrossRef  Google Scholar 

  10. Schmidt, R., et al.: Longitudinal change of small-vessel disease-related brain abnormalities. J. Cereb. Blood Flow Metab. 36(1), 26–39 (2016). https://doi.org/10.1038/jcbfm.2015.72

    CrossRef  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

    CrossRef  Google Scholar 

  13. Wardlaw, J.M., et al.: Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 12(8), 822–838 (2013). https://doi.org/10.1016/S1474-4422(13)70124-8

    CrossRef  Google Scholar 

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Acknowledgement

Funds from the Indonesia Endowment Fund for Education (LPDP), Ministry of Finance, Republic of Indonesia (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. The Titan Xp used for this research was donated by the NVIDIA Corporation.

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Correspondence to Muhammad Febrian Rachmadi .

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Rachmadi, M.F., del C. Valdés-Hernández, M., Makin, S., Wardlaw, J.M., Komura, T. (2019). Predicting the Evolution of White Matter Hyperintensities in Brain MRI Using Generative Adversarial Networks and Irregularity Map. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_17

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  • DOI: https://doi.org/10.1007/978-3-030-32248-9_17

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