Journal of Medical Systems

, 42:223 | Cite as

Multilayered Deep Structure Tensor Delaunay Triangulation and Morphing Based Automated Diagnosis and 3D Presentation of Human Macula

  • Taimur Hassan
  • M. Usman AkramEmail author
  • Mahmood Akhtar
  • Shoab Ahmad Khan
  • Ubaidullah Yasin
Image & Signal Processing
Part of the following topical collections:
  1. Image & Signal Processing


Maculopathy is the group of diseases that affects central vision of a person and they are often associated with diabetes. Many researchers reported automated diagnosis of maculopathy from optical coherence tomography (OCT) images. However, to the best of our knowledge there is no literature that presents a complete 3D suite for the extraction as well as diagnosis of macula. Therefore, this paper presents a multilayered convolutional neural networks (CNN) structure tensor Delaunay triangulation and morphing based fully autonomous system that extracts up to nine retinal and choroidal layers along with the macular fluids. Furthermore, the proposed system utilizes the extracted retinal information for the automated diagnosis of maculopathy as well as for the robust reconstruction of 3D macula of retina. The proposed system has been validated on 41,921 retinal OCT scans acquired from different OCT machines and it significantly outperformed existing state of the art solutions by achieving the mean accuracy of 95.27% for extracting retinal and choroidal layers, mean dice coefficient of 0.90 for extracting fluid pathology and the overall accuracy of 96.07% for maculopathy diagnosis. To the best of our knowledge, the proposed framework is first of its kind that provides a fully automated and complete 3D integrated solution for the extraction of candidate macula along with its fully automated diagnosis against different macular syndromes.


Optical coherence tomography Image processing Ophthalmology Pattern recognition Neural networks 



We are thankful to AFIO, Rawalpindi and Amanat Eye Hospital, Rawalpindi for providing us the dataset. We are also thankful to Vision and Image Processing Lab, Duke University for making their OCT datasets publicly available.


This work has been funded by Ignite National Technology Fund, Ministry of Information Technology, Government of Pakistan.

Compliance with Ethical Standards

Conflict of Interest

All authors declare that they don’t have any conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. 1.
    Snell, R. S., and Lemp, M. A., Clinical anatomy of the eye, 2nd edition, May 31st, 2013.CrossRefGoogle Scholar
  2. 2.
    "Implant gives rats sixth sense for infrared light". Wired UK. 14 February 2013. Accessed 14 February 2013.Google Scholar
  3. 3.
    Comers, G. M., Cystoid macular edema, Kellog Eye Center, Accessed June 2016.Google Scholar
  4. 4.
    Wang, M., Munch, I. C., Hasler, P. W., Prünte, C., and Larsen, M., Central serous chorioretinopathy. Acta Ophthalmol. 86(2):126–145, 2008.CrossRefGoogle Scholar
  5. 5.
    Saine P. J., Fundus Photography: What is a Fundus Camera? Ophthalmic Photographers Society Accessed March 30th, 2018.Google Scholar
  6. 6.
    Schuman, J. S., Introduction to Optical Coherence Tomography Technology.Google Scholar
  7. 7.
    Shrestha, A., Maharjan, N., Shrestha, A., Thapa, R., and Poudyal, G., Optical Coherence Tomographic assessment of macular thickness and morphological patterns in diabetic macular edema: Prognosis after modified grid photocoagulation. Nepal J. Ophthalmol. 4(7):128–133, 2012.Google Scholar
  8. 8.
    Zhang, W., Yamamoto, K., and Hori, S., Optical Coherence Tomography for assessment of diabetic macular edema. Int. J. Opthalmol. 1, December 18, 2008.Google Scholar
  9. 9.
    Hannouche, R. Z., and Ávila, M. P., Detection of diabetic foveal edema with bio microscopy, fluorescein angiography and optical coherence tomography. Arq. Bras. Oftalmol. 71(5):759–763, 2008.CrossRefGoogle Scholar
  10. 10.
    Mokwa, N. F., Ristau, T., Keane, P. A., Kirchhoff, B., Sadda, S. R., and Liakopoulos, S., Diagnosis of age-related macular degeneration: comparison between color fundus photography, fluorescein angiography, and spectral domain optical coherence tomography. J. Ophthalmol. 2013:6, 2013), Article ID 85915.Google Scholar
  11. 11.
    Georgieva, D. K., Optical coherence tomography findings in diabetic macular edema, February 24, 2012.Google Scholar
  12. 12.
    Helmy, Y. M., and Atta Allah, H. R., Optical coherence tomography classification of diabetic cystoid macular edema. Clinical Ophthalmology - Dove press, August 27, 2013.Google Scholar
  13. 13.
    Virgili, G., Menchini, F., Murro, V., Peluso, E., Rosa, F., Casazza, G., Optical coherence tomography (OCT) for detection of macular oedema in patients with diabetic retinopathy. Cochrane Database Syst. Rev. Jul 6;(7): CD008081. doi:, 2011.
  14. 14.
    Sikorski, B. L., Malukiewicz, G., Stafiej, J., Junk, H. L., and Raczynska, D., The Diagnostic Function of OCT in Diabetic Maculopathy, Mediators of Inflammation, Volume 2013, Article ID 434560, 12 pages, 2013.CrossRefGoogle Scholar
  15. 15.
    Trichonas, G., and Kaiser, P. K., Optical coherence tomography imaging of macular oedema. Br. J. Ophthalmol. 98:ii24–ii29, 2014. Scholar
  16. 16.
    Martidis, A., Duker, J. S., Greenberg, P. B, Rogers, A. H., Puliafito, C. A., Reichel, E., Baumal, C., Intravitreal triamcinolone for refractory diabetic macular edema. Elsevier J. Ophthalmol., doi:, 24th April 2002.CrossRefGoogle Scholar
  17. 17.
    Hee, M. R., Puliafito, C. A., Duker, J. S., Reichel, E., Coker, J. G., Wilkins, J. R., Schuman, J. S., Swanson, E. A., Fujimoto, J. G., Topography of diabetic macular edema with optical coherence tomography, Elsevier J Ophthalmol.. doi:, 14 March 2005.CrossRefGoogle Scholar
  18. 18.
    Zhang, L., Zhu, W., Shi, F., Chen, H., and Chen, X., Automated segmentation of intra-retinal cystoid macular edema for retinal 3D OCT images with macular hole. Int. Symp. Biomed. Imag. 12:1494–1497, 2015.Google Scholar
  19. 19.
    Wilkins, G. R., Houghton, O. M., and Oldenburg, A. L., Automated segmentation of intraretinal cystoid fluid in optical coherence tomography. IEEE Trans. Biomed. Eng. 59(4):1109–1114, 2012.CrossRefGoogle Scholar
  20. 20.
    Sugruk, J., Kiattisin, S., and Lasantitham, A. L., Automated classification between age-related macular degeneration and diabetic macular edema in OCT image using image segmentation. IEEE Biomed. Eng. Int. Conf. 2014.Google Scholar
  21. 21.
    Hassan, B., and Raja, G., Fully automated assessment of macular edema using optical coherence tomography (OCT) images. 2016 Int Conf Intell. Syst. Eng. (ICISE), 15th – 17th January 2016.Google Scholar
  22. 22.
    Fernández, D. C., Salinas, H. M., and Puliafito, C. A., Automated detection of retinal layer structures on optical coherence tomography images. Opt. Express 13(25):10200–10216, 2005.CrossRefGoogle Scholar
  23. 23.
    Chiu, S. J., Li, X. T., Nicholas, P., Toth, C. A., Izatt, J. A., and Farsiu, S., Automatic segmentation of seven retinal layers in SD-OCT images congruent with expert manual segmentation. Opt. Express 18(18):19413–19428, 2010.CrossRefGoogle Scholar
  24. 24.
    Yang, Q., Reisman, C. A., Wang, Z., Fukuma, Y., Hangai, M., Yoshimura, N., Tomidokoro, A., Araie, M., Raza, A. S., Hood, D. C., and Chan, K., Automated layer segmentation of macular OCT images using dual-scale gradient information. Opt. Express 18(20):21293–21307, 2010.CrossRefGoogle Scholar
  25. 25.
    Abhishek, A. M., Berendschot, T. T., Rao, S. V., and Dabir, S., Segmentation and analysis of retinal layers (ILM & RPE) in optical coherence tomography images with edema. IEEE Conf. Biomed. Eng. Sci. (IECBES). 204-209, 2014Google Scholar
  26. 26.
    Kaba, D., Wang, Y., Wang, C., Liu, X., Zhu, H., Salazar-Gonzalez, A. G., and Li, Y., Retina layer segmentation using kernel graph cuts and continuous max-flow. Opt. Express 23(6):7366–7384, 2015.CrossRefGoogle Scholar
  27. 27.
    Y. Huang, R. P. Danis, J. W. Pak, S. Luo, J. White, X. Zhang, A. Narkar and A. Domalpally, “Development of a semi-automatic segmentation method for retinal OCT images tested in patients with diabetic macular edema”, PLOS one, 8(12), p.e 82922, 2013.CrossRefGoogle Scholar
  28. 28.
    Srinivasan, P. P., Kim, L. A., Mettu, P. S., Cousins, S. W., Comer, G. M., Izatt, J. A., and Farsiu, S., Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed. Opt. Express 5(10)., 12 Sep 2014.CrossRefGoogle Scholar
  29. 29.
    Chiu, S. J., Allingham, M. J., Mettu, P. S., Cousins, S. W., Izatt, J. A., and Farsiu, S., Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema. Biomed. Opt. Express 6(4), 2015.CrossRefGoogle Scholar
  30. 30.
    Fang, L., Cunefare, D., Wang, C., Guymer, R. H., Li, S., and Farsiu, S., Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomed Opt. Express 8(5), 2017.CrossRefGoogle Scholar
  31. 31.
    Rashno, A., Koozekanani, D. D., Drayna, P. M., Nazari, B., Sadri, S., Rabbani, H., Parhi, K. K., Fully-automated segmentation of fluid/cyst regions in optical coherence tomography images with diabetic macular edema using neutrosophic sets and graph algorithms. IEEE Trans. Biomed. Eng., vol. PP, no. 99, pp. 1-1, 2017.Google Scholar
  32. 32.
    Lee, C. S., Tyring, A. J., Deruyter, N. P., Wu, Y., Rokem, A., and Lee, A. Y., Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomed. Opt. Express 8(7), 2017.Google Scholar
  33. 33.
    Hassan, T., Akram, M. U., Hassan, B., Syed, A. M., and Bazaz, S. A., Automated segmentation of subretinal layers for the detection of macular edema. Appl. Opt. 55:454–461, 2016.CrossRefGoogle Scholar
  34. 34.
    Hassan, B., Raja, G., Hassan, T., and Akram, M. U., Structure tensor based automated detection of macular edema and central serous retinopathy using optical coherence tomography images. J. Opt. Soc. Am. A 33:455–463, 2016.CrossRefGoogle Scholar
  35. 35.
    Syed, A. M., Hassan, T., Akram, M. U., Naz, S., and Khalid, S., Automated diagnosis of macular edema and central serous retinopathy through robust reconstruction of 3D retinal surfaces. Comput. Methods Prog. Biomed. 137:1–10, 2016.CrossRefGoogle Scholar
  36. 36.
    Badrinarayanan, V., Kendall, A., and Cipolla, R.. SegNet: A deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv: 1511.00561, 2015.Google Scholar
  37. 37.
    Krizhevsky, A., Sutskever, I., Hinton, G. E., imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NIPS), 2012.Google Scholar
  38. 38.
    Farsiu, S., Chiu, S. J., O’Connell, R. V., Folgar, F. A., Yuan, E., Izatt, J. A., and Toth, C. A., Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography. Ophthalmology, 121(1), 162-172 Jan. (2014).CrossRefGoogle Scholar
  39. 39.
    Delaunay, B., "Sur la sphère vide", Bulletin de l'Académie des Sciences de l'URSS. Classe des sciences mathématiques et naturelles. 6:793–800, 1934.Google Scholar
  40. 40.
    Rebay, S., Efficient unstructured mesh generation by means of delaunay triangulation and bowyer-watson algorithm. J. Comput. Phys. 106(1):127, 1993.CrossRefGoogle Scholar
  41. 41.
    Bengio, Y., Practical recommendations for gradient based training of deep architectures. Neural Networks: Tricks of the Trade, Springer, 437-478, 2012.Google Scholar
  42. 42.
    Murguia, M., and Villasenor, J. L., Estimating the effect of the similarity coefficient and the cluster algorithm on biogeographic classifications. Ann. Bot. Fennici. 40:415–421, 2003.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer & Software EngineeringNational University of Sciences and Technology (NUST)IslamabadPakistan
  2. 2.Department of Electrical EngineeringBahria UniversityIslamabadPakistan
  3. 3.School of Civil and Environmental Engineering’s Research Centre for Integrated Transport Innovation (rCITI)University of New South WalesSydneyAustralia
  4. 4.Department of OphthalmologyArmed Forces Institute of OphthalmologyRawalpindiPakistan

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