An Automatic Optic Disk Segmentation Approach from Retina of Neonates via Attention Based Deep Network

Document Type : Original Article


1 Department of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran

2 Department of Mechanical Engineering, Tehran University of Medical Science, Tehran, Iran

3 Department of Ophthalmology, Mashhad University of Medical Sciences, Mashhad, Iran

4 Department of Pediatrics, Tabriz University of Medical Science, Tabriz, Iran

5 Department of Pediatrics, Tehran University of Medical Science, Tehran, Iran


Every year, many newborns lose their sight to retinopathy of prematurity (ROP) worldwide. Despite its high prevalence and adverse consequences, periodic examinations can effectively prevent it. The use of an intelligent system enables physicians to avoid medical mistakes while examining newborns. The optic disk (OD) is an integral part of the retina for grading the severity and progression of ROP. Due to the uneven brightness and lack of a defined OD border, the use of retinal images of infants is very challenging for OD diagnosis. This paper provides an innovative model of OD segmentation based on attention gate. Initially, the images were collected and preprocessed and inputted into a novel deep convolutional neural network consisting of attention in skip connections. The architecture is comprised of a two-stage convolutional network. Different outputs are obtained from two individual branches of the original image and image features in the first stage. The outputs were concatenated to transfer into the post-processing stage to identify the area related to the OD. The final results based on the Dice coefficient (Dice) and the Intersection-Over-Union (IoU) were 94.22% and 86.1%, respectively.


Main Subjects

  1. Chen, J. and Smith, L.E., "Retinopathy of prematurity", Angiogenesis, Vol. 10, No. 2, (2007), 133-140,
  2. Lumley, J., "1 the epidemiology of preterm birth", Bailliere's Clinical Obstetrics Gynaecology, Vol. 7, No. 3, (1993), 477-498,
  3. Hartnett, M.E., "Pathophysiology and mechanisms of severe retinopathy of prematurity", Ophthalmology, Vol. 122, No. 1, (2015), 200-210,
  4. Tasman, W.S., "Revised indications for the treatment of retinopathy of prematurity: Results of the early treatment for retinopathy of prematurity randomized trial", Arch Ophthalmol, Vol. 121, (2003), 1684-1695,
  5. Stensvold, H.J., Klingenberg, C., Stoen, R., Moster, D., Braekke, K., Guthe, H.J., Astrup, H., Rettedal, S., Gronn, M. and Ronnestad, A.E., "Neonatal morbidity and 1-year survival of extremely preterm infants", Pediatrics, Vol. 139, No. 3, (2017),
  6. Aslam, T., Fleck, B., Patton, N., Trucco, M. and Azegrouz, H., "Digital image analysis of plus disease in retinopathy of prematurity", Acta Ophthalmologica, Vol. 87, No. 4, (2009), 368-377,
  7. Naqvi, S.S., Fatima, N., Khan, T.M., Rehman, Z.U. and Khan, M.A., "Automatic optic disk detection and segmentation by variational active contour estimation in retinal fundus images", Signal, Image Video Processing, Vol. 13, No. 6, (2019), 1191-1198,
  8. Fatima, K.N., Akram, M.U. and Bazaz, S.A., "Papilledema detection in fundus images using hybrid feature set", in 2015 5th International Conference on IT Convergence and Security (ICITCS), IEEE. (2015), 1-4.
  9. Walter, T., Klein, J.-C., Massin, P. and Erginay, A., "A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina", IEEE Transactions on Medical Imaging, Vol. 21, No. 10, (2002), 1236-1243,
  10. Welfer, D., Scharcanski, J., Kitamura, C.M., Dal Pizzol, M.M., Ludwig, L.W. and Marinho, D.R., "Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach", Computers in Biology Medicine, Vol. 40, No. 2, (2010), 124-137,
  11. Tjandrasa, H., Wijayanti, A. and Suciati, N., "Optic nerve head segmentation using hough transform and active contours", Telkomnika, Vol. 10, No. 3, (2012), 531,
  12. Abdullah, M., Fraz, M.M. and Barman, S.A., "Localization and segmentation of optic disc in retinal images using circular hough transform and grow-cut algorithm", PeerJ, Vol. 4, (2016), e2003,
  13. Thongnuch, V. and Uyyanonvara, B., "Automatic optic disk detection from low contrast retinal images of rop infant using gvf snake", Suranaree J Sci Technol, Vol. 14, No. 3, (2007), 223-234, doi.
  14. Pathan, S., Kumar, P., Pai, R. and Bhandary, S.V., "Automated detection of optic disc contours in fundus images using decision tree classifier", Biocybernetics Biomedical Engineering, Vol. 40, No. 1, (2020), 52-64,
  15. Mookiah, M.R.K., Acharya, U.R., Chua, C.K., Min, L.C., Ng, E., Mushrif, M.M. and Laude, A., "Automated detection of optic disk in retinal fundus images using intuitionistic fuzzy histon segmentation", Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, Vol. 227, No. 1, (2013), 37-49,
  16. ASADI, A.S., Hassanpour, H., Shahiri, M. and Ghaderi, R., "Detection of microaneurysms in retinal angiography images using the circular hough transform", (2012).
  17. Thongnuch, V. and Uyyanonvara, B., "Automatic detection of optic disc from fundus images of rop infant using 2d circular hough transform", Sirindhorn International Institute of Technology, Thammasat University, Thailand, Vol., (2006).
  18. Zahoor, M.N. and Fraz, M.M., "Fast optic disc segmentation in retina using polar transform", IEEE Access, Vol. 5, (2017), 12293-12300,
  19. Ghadiri, F., Bergevin, R. and Shafiee, M., "An adaptive thresholding approach for automatic optic disk segmentation", arXiv preprint arXiv:.05104, (2017).
  20. Septiarini, A., Harjoko, A., Pulungan, R. and Ekantini, R., "Optic disc and cup segmentation by automatic thresholding with morphological operation for glaucoma evaluation", Signal, Image Video Processing, Vol. 11, No. 5, (2017), 945-952, doi.
  21. Rehman, Z.U., Naqvi, S.S., Khan, T.M., Arsalan, M., Khan, M.A. and Khalil, M., "Multi-parametric optic disc segmentation using superpixel based feature classification", Expert Systems with Applications, Vol. 120, (2019), 461-473,
  22. Dutta, M.K., Mourya, A.K., Singh, A., Parthasarathi, M., Burget, R. and Riha, K., "Glaucoma detection by segmenting the super pixels from fundus colour retinal images", in 2014 international conference on medical imaging, m-health and emerging communication systems (MedCom), IEEE. (2014), 86-90.
  23. Morales, S., Naranjo, V., Angulo, J. and Alcañiz, M., "Automatic detection of optic disc based on pca and mathematical morphology", IEEE Transactions on Medical Imaging, Vol. 32, No. 4, (2013), 786-796, doi.
  24. Dharmawan, D.A., Ng, B.P. and Rahardja, S., "A new optic disc segmentation method using a modified dolph-chebyshev matched filter", Biomedical Signal Processing Control, Vol. 59, (2020), 101932,
  25. Sezavar, A., Farsi, H. and Mohamadzadeh, S., "A modified grasshopper optimization algorithm combined with cnn for content based image retrieval", International Journal of Engineering, Transactions A: Basics, Vol. 32, No. 7, (2019), 924-930,
  26. Feizi, A., "Convolutional gating network for object tracking", International Journal of Engineering, Transactions A: Basics, Vol. 32, No. 7, (2019), 931-939,
  27. Bagheria, F., Tarokh, M. and Ziaratbanb, M., "Semantic segmentation of lesions from dermoscopic images using yolo-deeplab networks", International Journal of Engineering, Transactions B: Applications, Vol. 34, No. 2, (2021), 458-469, doi.
  28. Sevastopolsky, A., "Optic disc and cup segmentation methods for glaucoma detection with modification of u-net convolutional neural network", Pattern Recognition Image Analysis, Vol. 27, No. 3, (2017), 618-624,
  29. Kim, J., Tran, L., Chew, E.Y., Antani, S. and Thoma, G.R., "Optic disc segmentation in fundus images using deep learning", in Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, International Society for Optics and Photonics. Vol. 10954, (2019), 109540H.
  30. Yu, S., Xiao, D., Frost, S. and Kanagasingam, Y., "Robust optic disc and cup segmentation with deep learning for glaucoma detection", Computerized Medical Imaging Graphics, Vol. 74, (2019), 61-71, doi.
  31. Juneja, M., Singh, S., Agarwal, N., Bali, S., Gupta, S., Thakur, N. and Jindal, P., "Automated detection of glaucoma using deep learning convolution network (g-net)", Multimedia Tools Applications, Vol. 79, (2020),
  32. Bhatkalkar, B.J., Reddy, D.R., Prabhu, S. and Bhandary, S.V., "Improving the performance of convolutional neural network for the segmentation of optic disc in fundus images using attention gates and conditional random fields", IEEE Access, Vol. 8, (2020), 29299-29310,
  33. Jiang, Y., Xia, H., Xu, Y., Cheng, J., Fu, H., Duan, L., Meng, Z. and Liu, J., "Optic disc and cup segmentation with blood vessel removal from fundus images for glaucoma detection", in 2018 40th annual international conference of the ieee engineering in medicine and biology society (EMBC), IEEE. (2018), 862-865.
  34. Zheng, Y., Zhang, X., Xu, X., Tian, Z. and Du, S., "Deep level set method for optic disc and cup segmentation on fundus images", Biomedical Optics Express, Vol. 12, No. 11, (2021), 6969-6983,
  35. Tabassum, M., Khan, T.M., Arsalan, M., Naqvi, S.S., Ahmed, M., Madni, H.A. and Mirza, J., "Cded-net: Joint segmentation of optic disc and optic cup for glaucoma screening", IEEE Access, Vol. 8, (2020), 102733-102747,
  36. Nisha, K., Sreelekha, G., Sathidevi, P., Mohanachandran, P. and Vinekar, A., "A robust algorithm for automated detection of optic disc in retina of premature infants", in TENCON 2019-2019 IEEE Region 10 Conference (TENCON), IEEE. (2019), 684-689.
  37. Alais, R., Dokládal, P., Erginay, A., Figliuzzi, B. and Decencière, E., "Fast macula detection and application to retinal image quality assessment", Biomedical Signal Processing Control, Vol. 55, (2020), 101567,
  38. Wang, L., Liu, H., Lu, Y., Chen, H., Zhang, J. and Pu, J., "A coarse-to-fine deep learning framework for optic disc segmentation in fundus images", Biomedical Signal Processing Control, Vol. 51, (2019), 82-89,
  39. Ke, P., Cai, M., Wang, H. and Chen, J., "A novel face recognition algorithm based on the combination of lbp and cnn", in 2018 14th IEEE International Conference on Signal Processing (ICSP), IEEE. (2018), 539-543.
  40. Touahri, R., AzizI, N., Hammami, N.E., Aldwairi, M. and Benaida, F., "Automated breast tumor diagnosis using local binary patterns (lbp) based on deep learning classification", in 2019 International Conference on Computer and Information Sciences (ICCIS), IEEE. (2019), 1-5.
  41. Badaghei, R., Hassanpour, H. and Askari, T., "Detection of bikers without helmet using image texture and shape analysis", International Journal of Engineering, Transactions C: Aspects, Vol. 34, No. 3, (2021), 650-655,
  42. Hellström, A., Hård, A., Chen, Y., Niklasson, A. and Albertsson-Wikland, K., "Ocular fundus morphology in preterm children. Influence of gestational age, birth size, perinatal morbidity, and postnatal growth", Investigative Ophthalmology Visual Science, Vol. 38, No. 6, (1997), 1184-1192.
  43. Feng, X., Nan, Y., Pan, J., Zou, R. and Shen, L., "Comparative study on optic disc features of premature infants and term infants", (2020),