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Towards a Glaucoma Risk Index Based on Simulated Hemodynamics from Fundus Images

  • José Ignacio OrlandoEmail author
  • João Barbosa Breda
  • Karel van Keer
  • Matthew B. Blaschko
  • Pablo J. Blanco
  • Carlos A. Bulant
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

Glaucoma is the leading cause of irreversible but preventable blindness in the world. Its major treatable risk factor is the intra-ocular pressure, although other biomarkers are being explored to improve the understanding of the pathophysiology of the disease. It has been recently observed that glaucoma induces changes in the ocular hemodynamics. However, its effects on the functional behavior of the retinal arterioles have not been studied yet. In this paper we propose a first approach for characterizing those changes using computational hemodynamics. The retinal blood flow is simulated using a 0D model for a steady, incompressible non Newtonian fluid in rigid domains. The simulation is performed on patient-specific arterial trees extracted from fundus images. We also propose a novel feature representation technique to comprise the outcomes of the simulation stage into a fixed length feature vector that can be used for classification studies. Our experiments on a new database of fundus images show that our approach is able to capture representative changes in the hemodynamics of glaucomatous patients. Code and data are publicly available in https://ignaciorlando.github.io.

Notes

Acknowledgements

This work is funded by ANPCyT PICTs 2016-0116 and start-up 2015-0006, FWO G0A2716N, WWTF VRG12-009 and a NVIDIA Hardware Grant. JIO is now with OPTIMA, Medical University of Vienna, Austria.

Supplementary material

473975_1_En_8_MOESM1_ESM.pdf (377 kb)
Supplementary material 1 (pdf 376 KB)

References

  1. 1.
    Tham, Y.C., et al.: Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology 121(11), 2081–2090 (2014)CrossRefGoogle Scholar
  2. 2.
    Harris, A., et al.: Ocular hemodynamics and glaucoma: the role of mathematical modeling. Eur. J. Ophthalmol. 23, 139–146 (2013)CrossRefGoogle Scholar
  3. 3.
    Barbosa-Breda, J., et al.: Clinical metabolomics and glaucoma. Ophthalmic Res. 59(1), 1–6 (2018)CrossRefGoogle Scholar
  4. 4.
    Abegão Pinto, L., et al.: Ocular blood flow in glaucoma-the Leuven Eye Study. Acta Ophthalmol. 94(6), 592–598 (2016)CrossRefGoogle Scholar
  5. 5.
    Lu, Y., et al.: Computational fluid dynamics assisted characterization of parafoveal hemodynamics in normal and diabetic eyes using adaptive optics scanning laser ophthalmoscopy. Biomed. Opt. Express 7(12), 4958 (2016)CrossRefGoogle Scholar
  6. 6.
    Liu, D., et al.: Image-based blood flow simulation in the retinal circulation. In: Vander Sloten, J., Verdonck, P., Nyssen, M., Haueisen, J. (eds.) ECIFMBE 2008. IFMBE Proceedings, vol. 22, pp. 1963–1966. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-540-89208-3_468CrossRefGoogle Scholar
  7. 7.
    Ganesan, P., He, S., Xu, H.: Analysis of retinal circulation using an image-based network model of retinal vasculature. Microvasc. Res. 80(1), 99–109 (2010)CrossRefGoogle Scholar
  8. 8.
    Caliva, F., et al.: Hemodynamics in the retinal vasculature during the progression of diabetic retinopathy. JMO 1(4), 6–15 (2017)Google Scholar
  9. 9.
    Li, F.-F., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: CVPR, vol. 2, pp. 524–531. IEEE (2005)Google Scholar
  10. 10.
    Moccia, S., et al.: Blood vessel segmentation algorithms-review of methods, datasets and evaluation metrics. CMPB 158, 71–91 (2018)Google Scholar
  11. 11.
    Giancardo, L., Roberts, K., Zhao, Z.: Representation learning for retinal vasculature embeddings. In: Cardoso, M.J., et al. (eds.) FIFI/OMIA -2017. LNCS, vol. 10554, pp. 243–250. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67561-9_28CrossRefGoogle Scholar
  12. 12.
    Rumpf, M., Telea, A.: A continuous skeletonization method based on level sets. In: Eurographics/IEEE VGTC Symposium on Visualization, pp. 151–159 (2002)Google Scholar
  13. 13.
    Maurer, C.R., Qi, R., Raghavan, V.: A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions. IEEE PAMI 25(2), 265–270 (2003)CrossRefGoogle Scholar
  14. 14.
    Pries, A.R., Secomb, T.W., Gaehtgens, P.: Biophysical aspects of blood flow in the microvasculature. Cardiovasc. Res. 32(4), 654–667 (1996)CrossRefGoogle Scholar
  15. 15.
    Blanco, P., Queiroz, R., Feijóo, R.: A computational approach to generate concurrent arterial networks in vascular territories. Int. J. Numer. Method Biomed. Eng. 29, 601–614 (2013)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Pournaras, C.J., Riva, C.E.: Retinal blood flow evaluation. Ophthalmologica 229(2), 61–74 (2013)CrossRefGoogle Scholar
  17. 17.
    Mitchell, P., et al.: Retinal vessel diameter and open-angle glaucoma: the Blue Mountains Eye Study. Ophthalmology 112(2), 245–250 (2005)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Abegão Pinto, L., Vandewalle, E., Stalmans, I.: Disturbed correlation between arterial resistance and pulsatility in glaucoma patients. Acta Ophthalmol. 90(3), e214–e220 (2012)CrossRefGoogle Scholar
  19. 19.
    Abegão Pinto, L., et al.: Lack of spontaneous venous pulsation: possible risk indicator in normal tension glaucoma? Acta Ophthalmol. 91(6), 514–520 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • José Ignacio Orlando
    • 1
    Email author
  • João Barbosa Breda
    • 2
  • Karel van Keer
    • 2
  • Matthew B. Blaschko
    • 3
  • Pablo J. Blanco
    • 4
  • Carlos A. Bulant
    • 1
  1. 1.CONICET - Pladema InstituteUNICENTandilArgentina
  2. 2.Research Group OphthalmologyKU LeuvenLeuvenBelgium
  3. 3.ESAT-PSIKU LeuvenLeuvenBelgium
  4. 4.National Laboratory for Scientific ComputingLNCC/MCTICPetrópolisBrazil

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