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Detection of retinal disorders from OCT images using generative adversarial networks

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Abstract

Retinal image analysis has opened up a new window for prompt diagnosis and detection of various retinal disorders. Optical Coherence Tomography (OCT) is one of the major diagnostic tools to identify retinal abnormalities related to macular disorders like Age-Related Macular Degeneration (AMD) and Diabetic Macular Edema (DME). The clinical findings include retinal layer analysis to spot the abnormalities on OCT images. Though various models are proposed over the years to diagnose these disorders automatically, an end-to-end system that performs automatic denoising, segmentation, and classification does not exist to the best of our knowledge. This paper proposes a Generative Adversarial Network (GAN) based approach for automated segmentation and classification of OCT-B scans to diagnose AMD and DME. The proposed method incorporates the integration of handcrafted Gabor features to enhance the retina layer segmentation and non-local denoising to remove speckle noise. The classification metrics of GAN are compared with existing methods. The accuracy of up to 92.42% and F1-score of 0.79 indicates that the GANs can perform well for segmentation and classification of OCT images.

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Notes

  1. The loss function used in a multi-class classification problem when the classes are mutually exclusive and are represented using integers.

  2. f(x) = max(x,0), where x is input

  3. f(x) = \(\frac {2}{1+e^{-2x}}\)

References

  1. 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 58:41–53. https://doi.org/10.1007/s11517-019-02066-y

    Article  Google Scholar 

  2. Arjovsky M, Chintala S, Bottou L (2017) Wasserstein GAN. arXiv e-prints arXiv:1701.07875. https://ui.adsabs.harvard.edu/abs/2017arXiv170107875A

  3. Balaji B, Jidesh P (2017) Non-local gradient fidelity model for multiplicative gamma noise removal. In: 2017 Ninth international conference on advances in pattern recognition (ICAPR). https://doi.org/10.1109/ICAPR.2017.8593110, pp 1–6

  4. Bandello F, Lattanzio R, Zucchiatti I, Arrigo A, Battista M, Cicinelli M V (2019) Diabetic macular edema: A step-by-step guide for ophthalmologists. Springer Nature, Switzerland, pp 97–183

    Book  Google Scholar 

  5. Birch DG, Liang FQ (2007) Age-related macular degeneration: A target for nanotechnology derived medicines. Int J Nanomedicine 2(1):65–77. https://doi.org/10.2147/nano.2007.2.1.65

    Article  Google Scholar 

  6. Brownlee J (2019) Generative adversarial networks with python:deep learning generative models for image synthesis and image translation. v1.5 edn. Machine Learning Mastery. https://books.google.co.in/books?id=YBimDwAAQBAJ

  7. Chiu SJ, Li XT, Nicholas P, Toth CA, Izatt JA, Farsiu S (2010) Automatic segmentation of seven retinal layers in sdoct images congruent with expert manual segmentation. Opt Express 18(18):19413–19428. https://doi.org/10.1364/OE.18.019413.

    Article  Google Scholar 

  8. Dai Z, Yang Z, Yang F, Cohen W W, Salakhutdinov R (2017) Good semi-supervised learning that requires a bad GAN. CoRR arXiv:abs/1705.09783

  9. Das V, Dandapat S, Bora PK (2020) A data-efficient approach for automated classification of oct images using generative adversarial network. IEEE Sensors Letters 4(1):1–4. https://doi.org/10.1109/LSENS.2019.2963712

    Article  Google Scholar 

  10. Dodo B I, Li Y, Kaba D, Liu X (2019) Retinal layer segmentation in optical coherence tomography images. IEEE Access 7:152388–152398. https://doi.org/10.1109/ACCESS.2019.2947761

    Article  Google Scholar 

  11. Febin I P, Jidesh P, Bini A A (2018) Noise classification and automatic restoration system using non-local regularization frameworks. Imaging Sci J 66(8):479–491. https://doi.org/10.1080/13682199.2018.1518760

    Article  Google Scholar 

  12. Froment J (2014) Parameter-free fast pixelwise non-local means denoising. Image Process 4:300–326. https://doi.org/10.5201/ipol.2014.120

    Article  Google Scholar 

  13. Fuglede B, Topsoe F (2004) Jensen-shannon divergence and hilbert space embedding. In: International symposium on information theory, 2004. ISIT 2004. Proceedings, p 31

  14. Garcia-Layana, Ciuffo A, Gianfranco, et al. (2017) Optical coherence tomography in age-related macular degeneration. https://amdbook.org/content/optical-coherence-tomography-age-related-macular-degenerationhttps://amdbook.org/content/optical-coherence-tomography-age-related-macular-degeneration. Accessed:20-01-2021

  15. Girish G N, Thakur B, Chowdhury S R, Kothari A R, Rajan J (2019) Segmentation of intra-retinal cysts from optical coherence tomography images using a fully convolutional neural network model. IEEE J Biomed Health Inform 23 (1):296–304. https://doi.org/10.1109/JBHI.2018.2810379

    Article  Google Scholar 

  16. Glen S (2014) P-value in statistical hypothesis tests: What is it?”. https://www.statisticshowto.com/p-value/. Accessed:20-01-2021

  17. Grandini M, Bagli E, Visani G (2020) Metrics for multi-class classification: An overview. arXiv:2008.05756. Accessed: 23-June-2021

  18. Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville A (2017) Improved training of wasserstein gans. In: Proceedings of the 31st international conference on neural information processing systems, NIPS’17. https://doi.org/10.5555/3295222.3295327. Curran Associates Inc., Red Hook, NY, USA, pp 5769–5779

  19. He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. CoRR arXiv:1512.033851512.03385

  20. Huang G, Liu Z, Weinberger K Q (2016) Densely connected convolutional networks. CoRR arXiv:abs/1608.06993

  21. Huang L, He X, Fang L, Rabbani H, Chen X (2019) Automatic classification of retinal optical coherence tomography images with layer guided convolutional neural network. IEEE Signal Process Lett 26(7):1026–1030. https://doi.org/10.1109/LSP.2019.2917779

    Article  Google Scholar 

  22. Jidesh P, Banothu B (2018) Image despeckling with non-local total bounded variation regularization. Comput Electr Eng 70:631–646. https://doi.org/10.1016/j.compeleceng.2017.09.013. http://www.sciencedirect.com/science/article/pii/S0045790617307619

    Article  Google Scholar 

  23. Kermany D, Zhang K, Goldbaum M (2018) Labeled optical coherence tomography (oct) and chest x-ray images for classification. Mendeley Data, v2. https://doi.org/10.17632/rscbjbr9sj.2, https://www.kaggle.com/paultimothymooney/kermany2018

  24. Li J, Jin P, Zhu J, Zou H, Xu X, Tang M, Zhou M, Gan Y, He J, Ling Y, Su Y (2021) Multi-scale gcn-assisted two-stage network for joint segmentation of retinal layers and discs in peripapillary oct images. Biomed Opt Express 12(4):2204–2220. https://doi.org/10.1364/BOE.417212. http://www.osapublishing.org/boe/abstract.cfm?URI=boe-12-4-2204

    Article  Google Scholar 

  25. Li X, Shen L, Shen M, Qiu C S (2019) Integrating handcrafted and deep features for optical coherence tomography based retinal disease classification. IEEE Access 7:33771–33777. https://doi.org/10.1109/ACCESS.2019.2891975

    Article  Google Scholar 

  26. Li QL et al (2020) Deepretina: Layer segmentation of retina in oct images using deep learning. Transl Vision Sci Technol 9(2):61. https://doi.org/10.1167/tvst.9.2.61

    Article  Google Scholar 

  27. Ma Y, Gao Y, Li Z, Li A, Wang Y, Liu J, Yu Y, Shi W, Ma Z (2021) Automated retinal layer segmentation on optical coherence tomography image by combination of structure interpolation and lateral mean filtering. J Innov Opt Health Sci 14(01):2140011. https://doi.org/10.1142/S1793545821400113

    Article  Google Scholar 

  28. Minitab (2020) Goodness of fit for individual distribution identification. https://support.minitab.com/en-us/minitab/19/help-and-how-to/quality-and-process-improvement/quality-tools/how-to/individual-distribution-identification/interpret-the-results/all-statistics-and-graphs/goodness-of-fit/. Accessed:20-01-2021

  29. Mishra Z et al (2020) Automated retinal layer segmentation using graph-based algorithm incorporating deep-learning-derived information. Scientific Reports 10:9541. https://doi.org/10.1038/s41598-020-66355-5

    Article  Google Scholar 

  30. Mittal A, Moorthy A K, Bovik A C (2012) No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 21 (12):4695–4708. https://doi.org/10.1109/TIP.2012.2214050

    Article  MathSciNet  MATH  Google Scholar 

  31. Moreira Neto C A, Rebhun C (2018) 1 - normal optical coherence tomography. In: Goldman D R, Waheed N K, Duker J S (eds) Atlas of Retinal OCT: Optical Coherence Tomography. http://www.sciencedirect.com/science/article/pii/B9780323461214000017. Elsevier, pp 1–15

  32. Negi A, Chauhan P, Kumar K, Rajput RS (2020) Face mask detection classifier and model pruning with keras-surgeon. In: 2020 5th IEEE international conference on recent advances and innovations in engineering (ICRAIE), pp 1–6

  33. Paul D, Tewari A, Ghosh S, Santosh K C (2020) Octx: Ensembled deep learning model to detect retinal disorders. In: 2020 IEEE 33rd international symposium on computer-based medical systems (CBMS). IEEE, pp 526–531

  34. Popescu DP, Choo-Smith LP et al (2011) Optical coherence tomography: Fundamental principles, instrumental designs and biomedical applications. Biophys Rev 3 (3):155. https://doi.org/10.1007/s12551-011-0054-7

    Article  Google Scholar 

  35. Rasti R, Rabbani H, Mehridehnavi A, Hajizadeh F (2018) Macular oct classification using a multi-scale convolutional neural network ensemble. IEEE Trans Med Imaging 37(4):1024–1034. https://doi.org/10.1109/TMI.2017.2780115

    Article  Google Scholar 

  36. Roy A G, Conjeti S, Karri S P K, Sheet D, Katouzian A, Wachinger C, Navab N (2017) Relaynet: Retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed Opt Express 8(8):3627–3642. https://doi.org/10.1364/BOE.8.003627. http://www.osapublishing.org/boe/abstract.cfm?URI=boe-8-8-3627

    Article  Google Scholar 

  37. Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X (2016) Improved techniques for training gans. In: Proceedings of the 30th international conference on neural information processing systems. NIPS’16. https://doi.org/10.5555/3157096.3157346. Curran Associates Inc., Red Hook, NY, USA, pp 2234–2242

  38. Schmitt J M, Xiang SH, Yung KM (1999) Speckle in optical coherence tomography. Journal of Biomedical Optics 4(1):95–105. https://doi.org/10.1117/1.429925

    Article  Google Scholar 

  39. Sedai S, Antony B, Rai R, Jones K, Ishikawa H, Schuman J, Gadi W, Garnavi R (2019) Uncertainty guided semi-supervised segmentation of retinal layers in oct images. In: Shen D, Liu T, Peters T M, Staib L H, Essert C, Zhou S, Yap P-T, Khan A (eds) Medical image computing and computer assisted intervention – MICCAI 2019. Springer International Publishing, Cham, pp 282–290

  40. Serener A, Serte S (2019) Dry and wet age-related macular degeneration classification using oct images and deep learning. In: 2019 scientific meeting on electrical-electronics biomedical engineering and computer science (EBBT), pp 1–4

  41. Sharma S, Shivhare S N, Singh N, Kumar K (2019) Computationally efficient ann model for small-scale problems. In: Tanveer M, Pachori R B (eds) Machine intelligence and signal analysis. Springer Singapore, Singapore, pp 423–435

  42. Sudeep PV, Issac Niwas S, Palanisamy P, Rajan J, Xiaojun Y, Wang X, Luo Y, Liu L (2016) Enhancement and bias removal of optical coherence tomography images: An iterative approach with adaptive bilateral filtering. Comput Biol Med 71:97–107. https://doi.org/10.1016/j.compbiomed.2016.02.003. http://www.sciencedirect.com/science/article/pii/S0010482516300300

    Article  Google Scholar 

  43. Sudeep PV, Palanisamy P, Rajan J, Baradaran H, Saba L, Gupta A, Suri J S (2016) Speckle reduction in medical ultrasound images using an unbiased non-local means method. Biomed Sig Process 28:1–8. https://doi.org/10.1016/j.bspc.2016.03.001. http://www.sciencedirect.com/science/article/pii/S1746809416300222

    Article  Google Scholar 

  44. Sunija AP, Saikat K, Gayathri S, Varun P G, Palanisamy P (2021) Octnet: A lightweight cnn for retinal disease classification from optical coherence tomography images. Comput Methods Prog Biomed 200:105877. https://doi.org/10.1016/j.cmpb.2020.105877. https://www.sciencedirect.com/science/article/pii/S0169260720317107

    Article  Google Scholar 

  45. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2015) Rethinking the inception architecture for computer vision. CoRR arXiv:1512.00567

  46. Teng P-y (2013) Caserel - an open source software for computer-aided segmentation of retinal layers in optical coherence tomography images. Zenodo, Public repository. https://doi.org/10.5281/zenodo.17893

  47. Van Hulle M, Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience. https://doi.org/10.1155/2016/3289801

  48. Waheed N K (2018) 15.1 - diabetic macular edema. Elsevier Goldman D R, Waheed N K, Duker J S (eds). http://www.sciencedirect.com/science/article/pii/B9780323461214000376

  49. Wang D, Wang L (2019) On oct image classification via deep learning. IEEE Photon J 11(5):1–14. https://doi.org/10.1109/JPHOT.2019.2934484

    Article  MathSciNet  Google Scholar 

  50. Weldon T P, Higgins W E, Dunn D F (1996) Efficient gabor filter design for texture segmentation. Pattern Recogn 29(12):2005–2015. https://doi.org/10.1016/S0031-3203(96)00047-7

    Article  Google Scholar 

  51. Xuehua W, Xiangcong X, Yaguang Z, Dingan H (2021) A new method with seu-net model for automatic segmentation of retinal layers in optical coherence tomography images. In: 2021 IEEE 2nd international conference on big data, artificial intelligence and internet of things engineering (ICBAIE), pp 260–263

  52. Yanagihara R T, Lee C S, Ting D S W, Lee A Y (2020) Methodological challenges of deep learning in optical coherence tomography for retinal diseases: a review. Transl Vision Sci Technol 9(2):11–11. https://doi.org/10.1167/tvst.9.2.11

    Article  Google Scholar 

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Acknowledgements

A. Smitha would like to acknowledge Prof. Vasudevan Lakshminarayanan, University of Waterloo, Canada, and Dr. J. Jothi Balaji, Medical Research Foundation, India, for their valuable suggestions regarding the research work. Smitha express her gratitude to the Ministry of Education, Government of India, for providing financial support (as fellowship) for carrying out the research at National Institute of Technology Karnataka, Surathkal. She also acknowledges the contribution of Mr. Abhinaba Hazarika, Master student at NITK, for assisting in the implementation and documentation of the proposed work. Dr. P. Jidesh thanks the Department of Atomic Energy, Govt. of India, for providing financial support under the research grant no. 02011/17/2020NBHM(RP)/R&DII/8073.

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  1. Department of Mathematical and Computational Sciences, National Institute of Technology Karnataka, Surathkal, 575025, India

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Appendix

Appendix

Consider a multi-category classification problem. The classification metrics of such a problem is elaborately explained in [17]. Let ‘C’ denote the number of classes. A classification matrix provides the mapping of expected outcome and the predicted outcome. Accordingly, we define the following terms from a multi-class confusion matrix.

  • TP - True Positive

  • TN - True Negative

  • FP - False Positive

  • NP - False Negative

$$ Sensitivity = {\frac{{\sum}^{C}_{k=1}{(TP_{k})}}{{\sum}^{C}_{k=1}{(TP_{k} + FN_{k})}}}, $$
(8)
$$ Specificity = {\frac{{\sum}^{C}_{k=1}{(TN_{k})}}{{\sum}^{C}_{k=1}{(TN_{k} + FP_{k})}}}, $$
(9)
$$ Accuracy = {\frac{{\sum}^{C}_{k=1}{(TP_{k} + TN_{k})}}{{\sum}^{C}_{k=1}{(TP_{k} + TN_{k} + FP_{k} + FN_{k})}}}, $$
(10)
$$ Precision = {\frac{{\sum}^{C}_{k=1}{(TP_{k})}}{{\sum}^{C}_{k=1}{(TP_{k} + FP_{k})}}}, $$
(11)
$$ Recall = {\frac{{\sum}^{C}_{k=1}{(TP_{k})}}{{\sum}^{C}_{k=1}{(FN_{k} + TP_{k})}}}, $$
(12)
$$ F1-Score = 2 * {\frac{Precision * Recall}{Precision + Recall}}. $$
(13)

Ideally, in classification problems related to the medical field, the number of true positives and true negatives have to be maximum, while the number of false positives, and false negatives have to be minimum. This implies that the sensitivity and specificity values must be close to 1 (100%). Sensitivity denotes the proportion of positives that are correctly identified, while specificity corresponds to the proportion of negatives that are correctly identified. Accuracy represents the precision of the classifier, or the ability to identify the positive and negative proportion precisely. A higher accuracy implies a better classifier. The precision, recall, and the F1-scores together signifies the ability of the classifier to accurately identify the true positives. A higher value of these indicates that the classifier performance is better.

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Smitha, A., Jidesh, P. Detection of retinal disorders from OCT images using generative adversarial networks. Multimed Tools Appl 81, 29609–29631 (2022). https://doi.org/10.1007/s11042-022-12475-1

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