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On the Uncertainty of Retinal Artery-Vein Classification with Dense Fully-Convolutional Neural Networks

Part of the Lecture Notes in Computer Science book series (LNIP,volume 12002)

Abstract

Retinal imaging is a valuable tool in diagnosing many eye diseases but offers opportunities to have a direct view to central nervous system and its blood vessels. The accurate measurement of the characteristics of retinal vessels allows not only analysis of retinal diseases but also many systemic diseases like diabetes and other cardiovascular or cerebrovascular diseases. This analysis benefits from precise blood vessel characterization. Automatic machine learning methods are typically trained in the supervised manner where a training set with ground truth data is available. Due to difficulties in precise pixelwise labeling, the question of the reliability of a trained model arises. This paper addresses this question using Bayesian deep learning and extends recent research on the uncertainty quantification of retinal vasculature and artery-vein classification. It is shown that state-of-the-art results can be achieved by using the trained model. An analysis of the predictions for cases where the class labels are unavailable is given.

Keywords

  • Bayesian deep learning
  • Blood vessels segmentation
  • Artery-vein classification

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References

  1. Almotiri, J., Elleithy, K., Elleithy, A.: Retinal vessels segmentation techniques and algorithms: a survey. Appl. Sci. 8(2), 155 (2018)

    CrossRef  Google Scholar 

  2. Badawi, S., Fraz, M.: Multiloss function based deep convolutional neural network for segmentation of retinal vasculature into arterioles and venules. BioMed Res. Int. 2019, 1–17 (2019). https://doi.org/10.1155/2019/4747230

    CrossRef  Google Scholar 

  3. Decencière, E., et al.: Feedback on a publicly distributed image database: the messidor database. Image Anal. Stereol. 33(3), 231–234 (2014)

    CrossRef  Google Scholar 

  4. Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050–1059 (2016)

    Google Scholar 

  5. Girard, F., Kavalec, C., Cheriet, F.: Joint segmentation and classification of retinal arteries/veins from fundus images. Artif. Intell. Med. 94, 96–109 (2019). https://doi.org/10.1016/j.artmed.2019.02.004

    CrossRef  Google Scholar 

  6. He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)

    Google Scholar 

  7. Hemelings, R., Elen, B., Stalmans, I., Van Keer, K., De Boever, P., Blaschko, M.B.: Artery-vein segmentation in fundus images using a fully convolutional network. Comput. Med. Imaging Graph. 76, 101636 (2019)

    CrossRef  Google Scholar 

  8. Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3), 203–210 (2000)

    CrossRef  Google Scholar 

  9. Hu, Q., Abràmoff, M.D., Garvin, M.K.: Automated separation of binary overlapping trees in low-contrast color retinal images. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 436–443. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_54

    CrossRef  Google Scholar 

  10. Hu, S., Worrall, D., Knegt, S., Veeling, B., Huisman, H., Welling, M.: Supervised uncertainty quantification for segmentation with multiple annotations. arXiv preprint arXiv:1907.01949 (2019)

  11. Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: fully convolutional DenseNets for semantic segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1175–1183. IEEE (2017)

    Google Scholar 

  12. Jogi, R.: Basic Ophthalmology. Jaypee Brothers Medical Publishers, New Delhi (2008)

    Google Scholar 

  13. Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems, pp. 5574–5584 (2017)

    Google Scholar 

  14. Malek, J., Tourki, R.: Blood vessels extraction and classification into arteries and veins in retinal images. In: 10th International Multi-conferences on Systems, Signals Devices 2013 (SSD 2013), pp. 1–6, March 2013

    Google Scholar 

  15. 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 

  16. Welikala, R., et al.: Automated arteriole and venule classification using deep learning for retinal images from the UK Biobank cohort. Comput. Biol. Med. 90, 23–32 (2017). https://doi.org/10.1016/j.compbiomed.2017.09.005

    CrossRef  Google Scholar 

  17. Zeiler, M.D.: ADADELTA: an adaptive learning rate method. Technical report, December 2012. arXiv: 1212.5701http://arxiv.org/abs/1212.5701

  18. Zhang, S., et al.: Simultaneous arteriole and venule segmentation of dual-modal fundus images using a multi-task cascade network. IEEE Access 7, 57561–57573 (2019). https://doi.org/10.1109/ACCESS.2019.2914319

    CrossRef  Google Scholar 

  19. Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Heckbert, P.S. (ed.) Graphics Gems IV, pp. 474–485. Academic Press Professional Inc., San Diego (1994). http://dl.acm.org/citation.cfm?id=180895.180940

    CrossRef  Google Scholar 

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Correspondence to Azat Garifullin .

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Garifullin, A., Lensu, L., Uusitalo, H. (2020). On the Uncertainty of Retinal Artery-Vein Classification with Dense Fully-Convolutional Neural Networks. In: Blanc-Talon, J., Delmas, P., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2020. Lecture Notes in Computer Science(), vol 12002. Springer, Cham. https://doi.org/10.1007/978-3-030-40605-9_8

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  • DOI: https://doi.org/10.1007/978-3-030-40605-9_8

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