Retinal Vessel Segmentation using Deep Neural Networks

Martina Melinscak, Pavle Prentasic, Sven Loncaric

Abstract

Automatic segmentation of blood vessels in fundus images is of great importance as eye diseases as well as some systemic diseases cause observable pathologic modifications. It is a binary classification problem: for each pixel we consider two possible classes (vessel or non-vessel). We use a GPU implementation of deep max-pooling convolutional neural networks to segment blood vessels. We test our method on publicly-available DRIVE dataset and our results demonstrate the high effectiveness of the deep learning approach. Our method achieves an average accuracy and AUC of 0.9466 and 0.9749, respectively.

References

  1. Abràmoff, M. D., Garvin, M. K., Sonka, M., 2010. Retinal Imaging and Image Analysis. IEEE Rev. Biomed. Eng. 3, 169-208. doi:10.1109/RBME.2010.2084567.
  2. Bühler, K., Felkel, P., La Cruz, A., 2004. Geometric methods for vessel visualization and quantification-a survey. Springer.
  3. Caffe | Deep Learning Framework [WWW Document], n.d. URL http://caffe.berkeleyvision.org/ (accessed 10.2.14).
  4. Ciresan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J., 2013. Mitosis detection in breast cancer histology images with deep neural networks, in: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2013. Springer, pp. 411-418.
  5. Ciresan, D. C., Meier, U., Masci, J., Maria Gambardella, L., Schmidhuber, J., 2011a. Flexible, high performance convolutional neural networks for image classification, in: IJCAI Proceedings-International Joint Conference on Artificial Intelligence. p. 1237.
  6. Ciresan, D. C., Meier, U., Masci, J., Maria Gambardella, L., Schmidhuber, J., 2011b. Flexible, high performance convolutional neural networks for image classification, in: IJCAI Proceedings-International Joint Conference on Artificial Intelligence. p. 1237.
  7. Ciresan, D., Meier, U., Schmidhuber, J., 2012. Multicolumn deep neural networks for image classification, in: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, pp. 3642-3649.
  8. Faust, O., Acharya, R., Ng, E.Y.-K., Ng, K.-H., Suri, J.S., 2012. Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review. J. Med. Syst. 36, 145-157.
  9. Felkel, P., Wegenkittl, R., Kanitsar, A., 2001. Vessel tracking in peripheral CTA datasets-an overview, in: Computer Graphics, Spring Conference On, 2001. IEEE, pp. 232-239.
  10. Fraz, M. M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A. R., Owen, C. G., Barman, S. A., 2012. Blood vessel segmentation methodologies in retinal images - A survey. Comput. Methods Programs Biomed. 108, 407-433. doi:10.1016/j.cmpb.2012.03.009.
  11. Fukushima, K., 1980. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36, 193-202.
  12. Giusti, A., Caccia, C., Ciresari, D.C., Schmidhuber, J., Gambardella, L.M., 2014. A comparison of algorithms and humans for mitosis detection, in: Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on. IEEE, pp. 1360-1363.
  13. Image Sciences Institute: DRIVE: Digital Retinal Images for Vessel Extraction [WWW Document], n.d. URL http://www.isi.uu.nl/Research/Databases/DRIVE/ (accessed 10.2.14).
  14. Kanski, J.J., Bowling, B., 2012. Synopsis of Clinical Ophthalmology. Elsevier Health Sciences.
  15. Kirbas, C., Quek, F., 2004. A review of vessel extraction techniques and algorithms. ACM Comput. Surv. CSUR 36, 81-121.
  16. Lindeberg, T., 1998. Edge detection and ridge detection with automatic scale selection. Int. J. Comput. Vis. 30, 117-156.
  17. Magnier, B., Aberkane, A., Borianne, P., Montesinos, P., Jourdan, C., 2014. Multi-scale crest line extraction based on half Gaussian Kernels, in: Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. IEEE, pp. 5105-5109.
  18. Masci, J., Giusti, A., Ciresan, D., Fricout, G., Schmidhuber, J., 2013. A fast learning algorithm for image segmentation with max-pooling convolutional networks. ArXiv Prepr. ArXiv13021690.
  19. Mitosis Detection in Breast Cancer Histological Images | IPAL UMI CNRS - TRIBVN - Pitié-Salpêtrière Hospital - The Ohio State University [WWW Document], n.d. URL http://ipal.cnrs.fr/ICPR2012/ (accessed 10.17.14).
  20. Niemeijer, M., Staal, J., van Ginneken, B., Loog, M., Abramoff, M.D., 2004. Comparative study of retinal vessel segmentation methods on a new publicly available database, in: Medical Imaging 2004. pp. 648-656.
  21. Patton, N., Aslam, T.M., MacGillivray, T., Deary, I.J., Dhillon, B., Eikelboom, R.H., Yogesan, K., Constable, I.J., 2006. Retinal image analysis: Concepts, applications and potential. Prog. Retin. Eye Res. 25, 99-127. doi:10.1016/j.preteyeres.2005.07.001.
  22. Ricci, E., Perfetti, R., 2007. Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification. IEEE Trans. Med. Imaging 26, 1357- 1365. doi:10.1109/TMI.2007.898551.
  23. Schmidhuber, J., 2014. Deep Learning in Neural Networks: An Overview. ArXiv Prepr. ArXiv14047828.
  24. Winder, R.J., Morrow, P.J., McRitchie, I.N., Bailie, J.R., Hart, P.M., 2009. Algorithms for digital image processing in diabetic retinopathy. Comput. Med. Imaging Graph. 33, 608-622.
Download


Paper Citation


in Harvard Style

Melinscak M., Prentasic P. and Loncaric S. (2015). Retinal Vessel Segmentation using Deep Neural Networks . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 577-582. DOI: 10.5220/0005313005770582


in Bibtex Style

@conference{visapp15,
author={Martina Melinscak and Pavle Prentasic and Sven Loncaric},
title={Retinal Vessel Segmentation using Deep Neural Networks},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={577-582},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005313005770582},
isbn={978-989-758-089-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Retinal Vessel Segmentation using Deep Neural Networks
SN - 978-989-758-089-5
AU - Melinscak M.
AU - Prentasic P.
AU - Loncaric S.
PY - 2015
SP - 577
EP - 582
DO - 10.5220/0005313005770582