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Research Article

Automated identification of SD-optical coherence tomography derived macular diseases by combining 3D-block-matching and deep learning techniques

ORCID Icon, ORCID Icon, ORCID Icon, &
Pages 660-669 | Received 07 Mar 2021, Accepted 03 May 2021, Published online: 09 Jun 2021

References

  • -Alqudah AM. 2020. AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images. Med Biol Eng Comput. 58(1):41–53. doi:https://doi.org/10.1007/s11517-019-02066-y.
  • Amil P, González L, Arrondo E, Salinas C, Guell JL, Masoller C, Parlitz U. 2019. Unsupervised feature extraction of anterior chamber OCT images for ordering and classification. Sci Rep. 9(1):1–9. doi:https://doi.org/10.1038/s41598-018-38136-8.
  • Ben Slama A, Mouelhi A, Sahli H, Zeraii A, Marrakchi J, Trabelsi H. 2020. A deep convolutional neural network for automated vestibular disorder classification using VNG analysis. Comput Methods Biomech Biomed Eng. 8(3):334–342.
  • Chen Q, Wu D. 2010. Image denoising by bounded block matching and 3D filtering. Sig Process. 90(9):2778–2783. doi:https://doi.org/10.1016/j.sigpro.2010.03.016.
  • Cheng J, Tao D, Quan Y, Wong DWK, Cheung GCM, Akiba M, Liu J. 2016. Speckle reduction in 3D optical coherence tomography of retina by A-scan reconstruction. IEEE Trans Med Imaging. 35(10):2270–2279. doi:https://doi.org/10.1109/TMI.2016.2556080.
  • Coscas G, Lupidi M, Coscas F. 2016. Heidelberg spectralis optical coherence tomography angiography: technical aspects. OCT Angiography Retinal Macular Dis. 56:1–5.
  • Du J, Vong CM, Chen CP. 2020. Novel efficient RNN and LSTM-like architectures: recurrent and gated broad learning systems and their applications for text classification. IEEE Trans Cybern. doi:https://doi.org/10.1109/TCYB.2018.2869476.
  • Fang L, Cunefare D, Wang C, Guymer RH, Li S, Farsiu S. 2017. 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):2732–2744. doi:https://doi.org/10.1364/BOE.8.002732.
  • Gallemore RP, Jumper MJ, McCuen BW 2nd, Jaffe GJ, Postel EA, Toth CA. 2000. Diagnosis of vitreoretinal adhesions in macular disease with optical coherence tomography. Retina (Philadelphia, Pa). 20(2):115–120. doi:https://doi.org/10.1097/00006982-200002000-00002.
  • Gerendas BS, Bogunovic H, Sadeghipour A, Schlegl T, Langs G, Waldstein SM, Schmidt-Erfurth U. 2017. Computational image analysis for prognosis determination in DME. Vision Res. 139:204–210. doi:https://doi.org/10.1016/j.visres.2017.03.008.
  • Guo P, Li D, Li X. 2020. Deep OCT image compression with convolutional neural networks. Biomed Opt Express. 11(7):3543–3554. doi:https://doi.org/10.1364/BOE.392882.
  • Hani M, Ben Slama A, Zghal I, Trabelsi H. 2020. Appropriate identification of age-related macular degeneration using OCT images. Comput Methods Biomech Biomed Eng Imag Visual. 9(2):146–156.
  • Huang Y, Danis RP, Pak JW, Luo S, White J, Zhang X, Domalpally A, Domalpally A. 2013. Development of a semi-automatic segmentation method for retinal OCT images tested in patients with diabetic macular edema. PloS One. 8(12):e82922. doi:https://doi.org/10.1371/journal.pone.0082922.
  • Hussain MA, Bhuiyan A, Turpin A, Luu CD, Smith RT, Guymer RH, Kotagiri R. 2016. Automatic identification of pathology-distorted retinal layer boundaries using SD-OCT imaging. IEEE Trans Biomed Eng. 64(7):1638–1649. doi:https://doi.org/10.1109/TBME.2016.2619120.
  • Ji Z, Chen Q, Niu S, Leng T, Rubin DL. 2018. Beyond retinal layers: a deep voting model for automated geographic atrophy segmentation in SD-OCT images. Transl Vis Sci Technol. 7(1): 1–1. doi:https://doi.org/10.1167/tvst.7.1.1.
  • Kamble RM, Chan GC, Perdomo O, Kokare M, Gonzalez FA, Müller H, Mériaudeau F. 2018. Automated diabetic macular edema (DME) analysis using fine tuning with inception-resnet-v2 on OCT images. In: 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES); Sarawak, Malaysia, December; IEEE. p. 442–446.
  • Kawasaki R, Yasuda M, Song SJ, Chen SJ, Jonas JB, Wang JJ, Wong TY, Wong TY. 2010. The prevalence of age-related macular degeneration in Asians: a systematic review and meta-analysis. Ophthalmology. 117(5):921–927. doi:https://doi.org/10.1016/j.ophtha.2009.10.007.
  • Kaymak S, Serener A. 2018. Automated age-related macular degeneration and diabetic macular edema detection on oct images using deep learning. In: 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP); September; Cluj-Napoca, Romania; IEEE. p. 265–269.
  • Koresh HJD, Chacko S. 2020. Hybrid speckle reduction filter for corneal OCT images. In: International Conference on Image Processing and Capsule Networks; May; Cham: Springer. p. 87–99.
  • Kornilov AS, Safonov IV. 2018. An overview of watershed algorithm implementations in open source libraries. J Imaging. 4(10):123.7. doi:https://doi.org/10.3390/jimaging4100123.
  • Lanzillo R, Cennamo G, Criscuolo C, Carotenuto A, Velotti N, Sparnelli F, Cianflone A, Moccia M, Brescia Morra V. 2018. Optical coherence tomography angiography retinal vascular network assessment in multiple sclerosis. Multiple Sclerosis J. 24(13):1706–1714. doi:https://doi.org/10.1177/1352458517729463.
  • Leal J, Luengo-Fernandez R, Stratton IM, Dale A, Ivanova K, Scanlon PH. 2019. Cost-effectiveness of digital surveillance clinics with optical coherence tomography versus hospital eye service follow-up for patients with screen-positive maculopathy. Eye. 33(4):640–647. doi:https://doi.org/10.1038/s41433-018-0297-7.
  • Lee CS, Tyring AJ, Deruyter NP, Wu Y, Rokem A, Lee AY. 2017. Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomed Opt Express. 8(7):3440–3448. doi:https://doi.org/10.1364/BOE.8.003440.
  • Leitgeb RA. 2019. En face optical coherence tomography: a technology review. Biomed Opt Express. 10(5):2177–2201. doi:https://doi.org/10.1364/BOE.10.002177.
  • Liao B, Xu J, Lv J, Zhou S. 2015. An image retrieval method for binary images based on DBN and softmax classifier. IETE Tech Rev. 32(4):294–303. doi:https://doi.org/10.1080/02564602.2015.1015631.
  • Mbarki Z, Seddik H, Braiek EB. 2016. A rapid hybrid algorithm for image restoration combining parametric Wiener filtering and wave atom transform. J Vis Commun Image Represent. 40:694–707. doi:https://doi.org/10.1016/j.jvcir.2016.08.009.
  • Motozawa N, An G, Takagi S, Kitahata S, Mandai M, Hirami Y, … Kurimoto Y. 2019. Optical coherence tomography-based deep-learning models for classifying normal and age-related macular degeneration and exudative and non-exudative age-related macular degeneration changes. Ophthalmol Ther. 8(4):527–539. doi:https://doi.org/10.1007/s40123-019-00207-y.
  • Nioi M, Napoli PE, Demontis R, Locci E, Fossarello M, d’Aloja E. 2018. Morphological analysis of corneal findings modifications after death: a preliminary OCT study on an animal model. Exp Eye Res. 169:20–27. doi:https://doi.org/10.1016/j.exer.2018.01.013.
  • Perdomo O, Rios H, Rodríguez FJ, Otálora S, Meriaudeau F, Müller H, González FA. 2019. Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography. Comput Methods Programs Biomed. 178:181–189. doi:https://doi.org/10.1016/j.cmpb.2019.06.016.
  • Pham DS, Venkatesh S. 2011. Improved image recovery from compressed data contaminated with impulsive noise. IEEE Trans Image Process. 21(1):397–405. doi:https://doi.org/10.1109/TIP.2011.2162418.
  • Rasti R, Rabbani H, Mehridehnavi A, Hajizadeh F. 2017. Macular OCT classification using a multi-scale convolutional neural network ensemble. IEEE Trans Med Imaging. 37(4):1024–1034. doi:https://doi.org/10.1109/TMI.2017.2780115.
  • Rathke F, Desana M, Schnörr C. 2017. Locally adaptive probabilistic models for global segmentation of pathological OCT scans. In: International Conference on Medical Image Computing and Computer-Assisted Intervention; September; Cham: Springer. p. 177–184.
  • Roberts PK, Vogl WD, Gerendas BS, Glassman AR, Bogunovic H, Jampol LM, Schmidt-Erfurth UM. 2020. Quantification of fluid resolution and visual acuity gain in patients with diabetic macular edema using deep learning: a post hoc analysis of a randomized clinical trial. JAMA Ophthalmol. 138(9):945–953. doi:https://doi.org/10.1001/jamaophthalmol.2020.2457.
  • Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Müller M. 2011. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 12(1):1–8. doi:https://doi.org/10.1186/1471-2105-12-77.
  • Shoji T, Yoshikawa Y, Kanno J, Ishii H, Ibuki H, Ozaki K, Kimura I, Shinoda K. 2018. Reproducibility of macular vessel density calculations via imaging with two different swept-source optical coherence tomography angiography systems. Transl Vis Sci Technol. 7(6): 31–31. doi:https://doi.org/10.1167/tvst.7.6.31.
  • Srinivasan PP, Kim LA, Mettu PS, Cousins SW, Comer GM, Izatt JA, Farsiu S. 2014. Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images. Biomed Opt Express. 5(10):3568–3577. doi:https://doi.org/10.1364/BOE.5.003568.
  • Stolte S, Fang R. 2020. A survey on medical image analysis in diabetic retinopathy. Med Image Anal. 64:101742. doi:https://doi.org/10.1016/j.media.2020.101742.
  • Tan B, Hosseinaee Z, Han L, Kralj O, Sorbara L, Bizheva K. 2018. 250 kHz, 1.5 µm resolution SD-OCT for in-vivo cellular imaging of the human cornea. Biomed Opt Express. 9(12):6569–6583. doi:https://doi.org/10.1364/BOE.9.006569.
  • Tan O, Chopra V, Lu ATH, Schuman JS, Ishikawa H, Wollstein G, Varma R, Huang D. 2009. Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography. Ophthalmology. 116(12):2305–2314. doi:https://doi.org/10.1016/j.ophtha.2009.05.025.
  • Tao LW, Wu Z, Guymer RH, Luu CD. 2016. Ellipsoid zone on optical coherence tomography: a review. Clin Experiment Ophthalmol. 44(5):422–430. doi:https://doi.org/10.1111/ceo.12685.
  • Tebini S, Mbarki Z, Seddik H, Braiek EB. 2016. Rapid and efficient image restoration technique based on new adaptive anisotropic diffusion function. Digit Signal Process. 48:201–215. doi:https://doi.org/10.1016/j.dsp.2015.09.013.
  • Treder M, Lauermann JL, Eter N. 2018. Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning. Graefe’s Arch Clin Exper Ophthalmol. 256(2):259–265. doi:https://doi.org/10.1007/s00417-017-3850-3.
  • Wang X, Tang F, Chen H, Luo L, Tang Z, Ran AR, Heng PA, Heng P-A. 2020. UD-MIL: uncertainty-driven deep multiple instance learning for OCT image classification. IEEE J Biomed Health Inf. 24(12):3431–3442. doi:https://doi.org/10.1109/JBHI.2020.2983730.
  • Yang D, Sun J. 2017. Bm3d-net: a convolutional neural network for transform-domain collaborative filtering. IEEE Signal Process Lett. 25(1):55–59. doi:https://doi.org/10.1109/LSP.2017.2768660.
  • Zou J, Li W, Du Q. 2015. Sparse representation-based nearest neighbor classifiers for hyperspectral imagery. IEEE Geosci Remote Sens Lett. 12(12):2418–2422. doi:https://doi.org/10.1109/LGRS.2015.2481181.

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