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GlaucoNet: Patch-Based Residual Deep Learning Network for Optic Disc and Cup Segmentation Towards Glaucoma Assessment


Glaucoma is a chronic eye condition causing irreversible vision damage and presently stands as the second leading cause of blindness worldwide. Damaged optic disc and optic cup assessment in color fundus image has been shown to be a promising method for glaucoma screening. In this paper, we propose a new accurate and efficient optic disc and cup segmentation methodology using a residual deep learning approach. The proposed patch-based deep network (GlaucoNet) is trained with a large number of preprocessed input image patches, big enough to include the important discriminatory information around each pixel. The proposed architecture gives better segmentation results as extra skip connections are introduced in the framework which explicitly reformulate the layers such that the learning function is dependent on a residual function of the input layer. It addresses the degradation problem of the deep network by improving the information flow. The convex hull transformation is applied on the initial results to obtain the final segmentation output. The proposed method is tested on publicly available DRISHTI-GS, RIM-ONE and ORIGA-light datasets. The experimental results for optic disc segmentation show an overlapping score (OS) of 0.9106, 0.8972 and 0.8835 and the optic cup segmentation achieves an OS of 0.8229, 0.7401 and 0.8106 in DRISHTI-GS, RIM-ONE and ORIGA-light, respectively. Finally, the glaucoma risk index is obtained by computing the cup-to-disc height ratio obtained from the segmented regions. Experimental results showing better or comparable segmentation performance and decrease in cup-to-disc height ratio estimation errors demonstrate the efficacy of the proposed GlaucoNet-based segmentation methodology.

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  1. Acharya UR, Bhat S, Koh JE, Bhandary SV, Adeli H. A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images. Comput Biol Med. 2017;88(Supplement C):72–83.

    Article  Google Scholar 

  2. Acharya UR, Dua S, Du X, Vinitha Sree S, Chua CK. Automated diagnosis of glaucoma using texture and higher order spectra features. IEEE Trans Inf Technol Biomed. 2011;15(3):449–55.

    Article  Google Scholar 

  3. Ali R, Sheng B, Li P, Chen Y, Li H, Yang P, Jung Y, Kim J, Chen CP. Optic disc and cup segmentation through fuzzy broad learning system for glaucoma screening. IEEE Trans Ind Inform. 2021;17(4):2476–87.

    Article  Google Scholar 

  4. Aquino A, Gegundez-Arias ME, Marin D. Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques. IEEE Trans Med Imaging. 2010;29(11):1860–9.

    Article  Google Scholar 

  5. Asem MM, Oveisi IS, Janbozorgic M. Blood vessel segmentation in modern wide-field retinal images in the presence of additive gaussian noise. J Med Imaging. 2018;5(3):031405.

    Article  Google Scholar 

  6. Cheng J, Liu J, Xu Y, Yin F, Wong DWK, Tan NM, Tao D, Cheng CY, Aung T, Wong TY. Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans Med Imaging. 2013;32(6):1019–32.

    Article  Google Scholar 

  7. Cheng J, Yin F, Wong DWK, Tao D, Liu J. Sparse dissimilarity-constrained coding for glaucoma screening. IEEE Trans Biomed Eng. 2015;62(5):1395–403.

    Article  Google Scholar 

  8. Chrástek R, Wolf M, Donath K, Niemann H, Paulus D, Hothorn T, Lausen B, Lämmer R, Mardin C, Michelson G. Automated segmentation of the optic nerve head for diagnosis of glaucoma. Med Image Anal. 2005;9(4):297–314.

    Article  Google Scholar 

  9. Dai B, Wu X, Bu W. Optic disc segmentation based on variational model with multiple energies. Pattern Recognit. 2017;64:226–35.

    Article  Google Scholar 

  10. Damon WWK, Liu J, Meng TN, Fengshou Y, Yin WT. Automatic detection of the optic cup using vessel kinking in digital retinal fundus images. In: 2012 9th IEEE international symposium on biomedical imaging (ISBI); 2012. p. 1647–50.

  11. Fantin G, Conrad K, Farida C. Statistical atlas-based descriptor for an early detection of optic disc abnormalities. J Med Imaging. 2018;5(1):014006.

    Article  Google Scholar 

  12. Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge: MIT Press; 2016

  13. Guo Y, Zou B, Chen Z, He Q, Liu Q, Zhao R. Optic cup segmentation using large pixel patch based CNNs.  In: Proceedings of the ophthalmic medical image analysis, third international workshop, OMIA held in conjunction with MICCAI; 2016. p. 129–36.

  14. Haleem MS, Han L, van Hemert J, Li B. Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: a review. Comput Med Imaging Graph. 2013;37(7):581–96.

    Article  Google Scholar 

  15. Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin P, Larochelle H. Brain tumor segmentation with deep neural networks. Med Image Anal. 2017;35:18–31.

    Article  Google Scholar 

  16. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR); 2016. p. 770–8.

  17. Issac A, Parthasarthi M, Dutta MK. An adaptive threshold based algorithm for optic disc and cup segmentation in fundus images. In: 2015 2nd international conference on signal processing and integrated networks (SPIN); 2015. p. 143–7.

  18. Jiang Y, Tan N, Peng T. Optic disc and cup segmentation based on deep convolutional generative adversarial networks. IEEE Access. 2019;7:64483–93.

    Article  Google Scholar 

  19. Jiang Z, Zhang H, Wang Y, Ko SB. Retinal blood vessel segmentation using fully convolutional network with transfer learning. Comput Med Imaging Graph. 2018;68:1–15.

    Article  Google Scholar 

  20. Joshi GD, Sivaswamy J, Karan K, Krishnadas SR. Optic disk and cup boundary detection using regional information. In: 2010 IEEE international symposium on biomedical imaging: from nano to macro; 2010. p. 948–51.

  21. Joshi GD, Sivaswamy J, Krishnadas SR. Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment. IEEE Trans Med Imaging. 2011;30(6):1192–205.

    Article  Google Scholar 

  22. Joshi GD, Sivaswamy J, Krishnadas SR. Depth discontinuity-based cup segmentation from multiview color retinal images. IEEE Trans Biomed Eng. 2012;59(6):1523–31.

    Article  Google Scholar 

  23. Lalonde M, Beaulieu M, Gagnon L. Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching. IEEE Trans Med Imaging. 2001;20(11):1193–200.

    Article  Google Scholar 

  24. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.

    Article  Google Scholar 

  25. Lim T, Chattopadhyay S, Acharya UR. A survey and comparative study on the instruments for glaucoma detection. Med Eng Phys. 2012;34(2):129–39.

    Article  Google Scholar 

  26. Linn A. Microsoft researchers win ImageNet computer vision challenge; 2015.

  27. Liskowski P, Krawiec K. Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging. 2016;35(11):2369–80.

    Article  Google Scholar 

  28. Lowell J, Hunter A, Steel D, Basu A, Ryder R, Fletcher E, Kennedy L. Optic nerve head segmentation. IEEE Trans Med Imaging. 2004;23(2):256–64.

    Article  Google Scholar 

  29. Lu S. Accurate and efficient optic disc detection and segmentation by a circular transformation. IEEE Trans Med Imaging. 2011;30(12):2126–33.

    Article  Google Scholar 

  30. Maheshwari S, Pachori RB, Acharya UR. Automated diagnosis of glaucoma using empirical wavelet transform and correntropy features extracted from fundus images. IEEE J Biomed Health Inform. 2017;21(3):803–13.

    Article  Google Scholar 

  31. Mittapalli PS, Kande GB. Segmentation of optic disk and optic cup from digital fundus images for the assessment of glaucoma. Biomed Signal Process Control. 2016;24:34–46.

    Article  Google Scholar 

  32. Nuzzi R, Marolo P, Nuzzi A. The hub-and-spoke management of glaucoma. Front Neurosci. 2020;14:180.

    Article  Google Scholar 

  33. Panda R, Puhan N, Panda G. New binary hausdorff symmetry measure based seeded region growing for retinal vessel segmentation. Biocybern Biomed Eng. 2016;36(1):119–29.

    Article  Google Scholar 

  34. Panda R, Puhan NB, Rao A, Mandal B, Padhy D, Pandaa G. Deep convolutional neural network-based patch classification for retinal nerve fiber layer defect detection in early glaucoma. J Med Imaging. 2018;5(4):044003.

    Article  Google Scholar 

  35. Roychowdhury S, Koozekanani DD, Kuchinka SN, Parhi KK. Optic disc boundary and vessel origin segmentation of fundus images. IEEE J Biomed Health Inform. 2016;20(6):1562–74.

    Article  Google Scholar 

  36. Sekhar S, Al-Nuaimy W, Nandi AK. Automated localisation of retinal optic disk using hough transform. In: 2008 5th IEEE international symposium on biomedical imaging: from nano to macro; 2008. p. 1577–80.

  37. Sevastopolsky A. Optic disc and cup segmentation methods for glaucoma detection with modification of u-net convolutional neural network. Pattern Recognit Image Anal. 2017;27(3):618–24.

    Article  Google Scholar 

  38. Shankaranarayana SM, Ram K, Mitra K, Sivaprakasam M. Fully convolutional networks for monocular retinal depth estimation and optic disc-cup segmentation. IEEE J Biomed Health Inform. 2019;23(4):1417–26.

    Article  Google Scholar 

  39. Singh A, Dutta MK, ParthaSarathi M, Uher V, Burget R. Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image. Comput Methods Programs Biomed. 2016;124:108–20.

    Article  Google Scholar 

  40. Sivaswamy J, Chakravarty A, Joshi G, Tabish AS, Krishnadas S. A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis. JSM Biomed Imaging Data Pap. 2015;2(1):1004.

    Google Scholar 

  41. Sivaswamy J, Krishnadas SR, Joshi GD, Jain M, Tabish AUS. Drishti-GS: retinal image dataset for optic nerve head (ONH) segmentation. In: 2014 IEEE 11th international symposium on biomedical imaging (ISBI); 2014. p. 53–6.

  42. Wan L, Zeiler M, Zhang S, Cun YL, Fergus R. Regularization of neural networks using dropconnect. In: Proceedings of the 30th international conference on machine learning, proceedings of machine learning research, 2013;28(3):1058–66.

  43. Wong DWK, Liu J, Lim JH, Jia X, Yin F, Li H, Wong TY. Level-set based automatic cup-to-disc ratio determination using retinal fundus images in argali. In: 2008 30th annual international conference of the IEEE engineering in medicine and biology society; 2008. p. 2266–9.

  44. Xu J, Chutatape O, Sung E, Zheng C, Kuan PCT. Optic disk feature extraction via modified deformable model technique for glaucoma analysis. Pattern Recognit. 2007;40(7):2063–76.

    Article  Google Scholar 

  45. Xu Y, Duan L, Lin S, Chen X, Wong DWK, Wong TY, Liu J. Optic cup segmentation for glaucoma detection using low-rank superpixel representation. In: International conference on medical image computing and computer-assisted intervention 2014. p. 788–95.

  46. Yin F, Liu J, Wong DWK, Tan NM, Cheng J, Cheng CY, Tham YC, Wong TY. Sector-based optic cup segmentation with intensity and blood vessel priors. In: Annual international conference of the IEEE engineering in medicine and biology society; 2012. p. 1454–7.

  47. Yu S, Xiao D, Frost S, Kanagasingam Y. Robust optic disc and cup segmentation with deep learning for glaucoma detection. Comput Med Imaging Graph. 2019;74:61–71.

    Article  Google Scholar 

  48. Zhang Z, Yin FS, Liu J, Wong WK, Tan NM, Lee BH, Cheng J, Wong TY. ORIGA\(^{\text{-light}}\): an online retinal fundus image database for glaucoma analysis and research. In: 2010 Annual international conference of the IEEE engineering in medicine and biology; 2010. p. 3065–8.

  49. Zilly J, Buhmann JM, Mahapatra D. Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation. Comput Med Imaging Graph. 2017;55:28–41 (Special Issue on Ophthalmic Medical Image Analysis).

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Correspondence to N. B. Puhan.

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Panda, R., Puhan, N.B., Mandal, B. et al. GlaucoNet: Patch-Based Residual Deep Learning Network for Optic Disc and Cup Segmentation Towards Glaucoma Assessment. SN COMPUT. SCI. 2, 99 (2021).

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  • Glaucoma
  • Optic disc and cup segmentation
  • Glaucoma screening
  • Deep learning
  • Residual network