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Automated retinal layer segmentation in OCT images of age-related macular degeneration

Automated retinal layer segmentation in OCT images of age-related macular degeneration

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Age-related macular degeneration (AMD) is a common eye disease that causes progressive degeneration of the central vision. The presence of abundant drusen is a common early feature of AMD. Optical coherence tomography (OCT) can provide detailed structure information on drusen. The physiological structure of the retinal epithelium and drusen complex (RPEDC) and the Bruch's membrane (BM) layer boundaries will be influenced by the presence of drusen with AMD. Therefore, drusen quantification is important to diagnose and cure AMD. The authors proposed an automatic method to segment the inner limiting membrane, the retinal pigment epithelium and drusen complex (RPEDC) and BM layer boundaries from OCT images with AMD (termed as deep forest for layer segmentation (DF-LS)). In their method, image patches are extracted and used to train a deep-forest model to predict three boundary probability maps. In addition, they modify grapy theory and dynamic programming method to find the layer boundary. Finally, the layer boundary is smoothed by using a smoothing operation. The proposed DF-LS method is evaluated on three publicly available datasets (one healthy dataset and two AMD dataset). The proposed DF-LS method can yield superior mean unsigned error with an average error of 0.81 pixel on Tian et al.'s dataset, and 1.35, 1.23 pixel on Chiu et al.'s and Farisu et al.'s dataset, respectively.

References

    1. 1)
      • 1. Schuman, S.G., Koreishi, A.F., Farsiu, S., et al: ‘Photoreceptor layer thinning over drusen in eyes with age-related macular degeneration imaged in vivo with spectral-domain optical coherence tomography’, Ophthalmology, 2009, 116, (3), pp. 488496. e482.
    2. 2)
      • 2. Esmaeili, M., Rabbani, H., Dehnavi, A., et al: ‘Automatic detection of exudates and optic disk in retinal images using curvelet transform’, IET Image Process., 2012, 6, (7), pp. 10051013.
    3. 3)
      • 3. Huang, D., Swanson, E.A., Lin, C.P., et al: ‘Optical coherence tomography’, Science, 1991, 254, (5035), pp. 11781181.
    4. 4)
      • 4. Chen, Z., Mo, Y., Ouyang, P., et al: ‘Retinal vessel optical coherence tomography images for anemia screening’, Med. Biol. Eng. Comput., 2019, 57, (4), pp. 953966.
    5. 5)
      • 5. de Sisternes, L., Jonna, G., Moss, J., et al: ‘Auto-mated intraretinal segmentation of SD-OCT images in normal and age-related macular degeneration eyes’, Biomed. Opt. Express, 2017, 8, (3), pp. 19261949.
    6. 6)
      • 6. DeBuc, D.C.: ‘A review of algorithms for segmentation of retinal image data using optical coherence tomography’, in ‘Image segmentation’ (InTech, Rijeka, Croatia, 2011), pp. 1554.
    7. 7)
      • 7. Novosel, J., Thepass, G., Lemij, H.G., et al: ‘Loosely coupled level sets for simultaneous 3D retinal layer segmentation in optical coherence tomography’, Med. Image Anal., 2015, 26, (1), pp. 146158.
    8. 8)
      • 8. Novosel, J., Wang, Z., De Jong, H., et al: ‘Loosely coupled level sets for retinal layers and drusen segmentation in subjects with dry age-related macular degeneration’. Medical Imaging 2016: Image Processing, San Diego, USA, 2016, p. 97842.
    9. 9)
      • 9. Chiu, S.J., Li, X.T., Nicholas, P., et al: ‘Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation’, Biomed. Opt. Express, 2010, 18, (18), pp. 1941319428.
    10. 10)
      • 10. Chiu, S.J., Izatt, J.A., O'Connell, R.V., et al: ‘Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images’, Invest. Ophthalmol. Vis. Sci., 2012, 53, (1), pp. 5361.
    11. 11)
      • 11. Zhang, T., Song, Z., Wang, X., et al: ‘Fast retinal layer segmentation of spectral domain optical coherence tomography images’, J. Biomed. Opt., 2015, 20, (9), p. 096014.
    12. 12)
      • 12. Dufour, P.A., Ceklic, L., Abdillahi, H., et al: ‘Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints’, IEEE Trans. Med. Imaging, 2013, 32, (3), pp. 531543.
    13. 13)
      • 13. Oliveira, J., Pereira, S., Gonçalves, L., et al: ‘Sparse high order potentials for extending multi-surface segmentation of OCT images with drusen’. 2015 37th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, 2015, pp. 29522955.
    14. 14)
      • 14. Oliveira, J., Pereira, S., Goncalves, L., et al: ‘Multi-surface segmentation of OCT images with AMD using sparse high order potentials’, Biomed. Opt. Express, 2017, 8, (1), pp. 281297.
    15. 15)
      • 15. Shah, A., Bai, J.J., Hu, Z.H., et al: ‘Multiple surface segmentation using truncated convex priors’, Med. Image Comput. Comput. Assist. Interv. III, 2015, 9351, pp. 97104.
    16. 16)
      • 16. Shah, A., Abramoff, M.D., Wu, X.: ‘Simultaneous multiple surface segmentation using deep learning’. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support’, Springer, Cham, 2017, pp. 311.
    17. 17)
      • 17. Shah, A., Zhou, L., Abrámoff, M.D., et al: ‘Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images’, Biomed. Opt. Express, 2018, 9, (9), pp. 45094526.
    18. 18)
      • 18. Fang, L., Cunefare, D., Wang, C., et al: ‘Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search’, Biomed. Opt. Express, 2017, 8, (5), pp. 27322744.
    19. 19)
      • 19. Chakravarty, A., Sivaswamy, J.: ‘End-to-end learning of a conditional random field for intra-retinal layer segmentation in optical coherence tomography’. Conf. Medical Image Understanding and Analysis, Springer, Cham, 2017, pp. 314.
    20. 20)
      • 20. Chakravarty, A., Sivaswamy, J.: ‘A supervised joint multi-layer segmentation framework for retinal optical coherence tomography images using conditional random field’, Comput. Methods Programs Biomed., 2018, 165, pp. 235250.
    21. 21)
      • 21. Rathke, F., Desana, M., Schnörr, C.: ‘Locally adaptive probabilistic models for global segmentation of pathological OCT scans’. Int. Conf. on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 2017, pp. 177184.
    22. 22)
      • 22. Gopinath, K., Rangrej, S.B., Sivaswamy, J.: ‘A deep learning framework for segmentation of retinal layers from OCT images’, arXiv preprint arXiv:1806.08859, 2017 4th IAPR Asian Conf. on Pattern Recognition (ACPR), Nanjing, China, 2017, pp. 888893.
    23. 23)
      • 23. Zhou, Z.-H., Feng, J.: ‘Deep forest: towards an alternative to deep neural networks’, arXiv preprint arXiv:1702.08835, Proceedings of the 26th Int. Joint Conf. on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp. 35533559.
    24. 24)
      • 24. Dijkstra, E.W.: ‘A note on two problems in connexion with graphs’, Numer. Math., 1959, 1, (1), pp. 269271.
    25. 25)
      • 25. Bellman, R.: ‘On a routing problem’, Quarterly Appl Math, 1958, 16, (1), pp. 8790.
    26. 26)
      • 26. Pearl, J.: ‘Heuristics: intelligent search strategies for computer problem solving’. 1984.
    27. 27)
      • 27. Tian, J., Varga, B., Somfai, G.M., et al: ‘Real-time automatic segmentation of optical coherence tomography volume data of the macular region’, PLOS One, 2015, 10, (8), p. e0133908.
    28. 28)
      • 28. Farsiu, S., Chiu, S.J., O'Connell, R.V., et al: ‘Quantitative classification of eyes with and without intermediate age-related macular degeneration using optical coherence tomography’, Ophthalmology, 2014, 121, (1), pp. 162172.
    29. 29)
      • 29. Mayer, M.: ‘OCTSEG (version v0.4)’. Available at https://www5.cs.fau.de/research/software/octseg/, accessed January 2016.
    30. 30)
      • 30. Mayer, M.A., Hornegger, J., Mardin, C.Y., et al: ‘Retinal nerve fiber layer segmentation on FD-OCT scans of normal subjects and glaucoma patients’, Biomed. Opt. Express, 2010, 1, (5), pp. 13581383.
    31. 31)
      • 31. Hussain, M.A., Bhuiyan, A., Turpin, A., et al: ‘Automatic identification of pathology-distorted retinal layer boundaries using SD-OCT imaging’, IEEE Trans. Biomed. Eng., 2017, 64, (7), pp. 16381649.
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