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Detecting glaucoma based on spectral domain optical coherence tomography imaging of peripapillary retinal nerve fiber layer: a comparison study between hand-crafted features and deep learning model

  • Glaucoma
  • Published:
Graefe's Archive for Clinical and Experimental Ophthalmology Aims and scope Submit manuscript

Abstract

Purpose

To develop a deep learning (DL) model for automated detection of glaucoma and to compare diagnostic capability against hand-craft features (HCFs) based on spectral domain optical coherence tomography (SD-OCT) peripapillary retinal nerve fiber layer (pRNFL) images.

Methods

A DL model with pre-trained convolutional neural network (CNN) based was trained using a retrospective training set of 1501 pRNFL OCT images, which included 690 images from 153 glaucoma patients and 811 images from 394 normal subjects. The DL model was further tested in an independent test set of 50 images from 50 glaucoma patients and 52 images from 52 normal subjects. A customized software was used to extract and measure HCFs including pRNFL thickness in average and four different sectors. Area under the receiver operator characteristics (AROC) curves was calculated to compare the diagnostic capability between DL model and hand-crafted pRNFL parameters.

Results

In this study, the DL model achieved an AROC of 0.99 [CI: 0.97 to 1.00] which was significantly larger than the AROC values of all other HCFs (AROCs 0.661 with 95% CI 0.549 to 0.772 for temporal sector, AROCs 0.696 with 95% CI 0.549 to 0.799 for nasal sector, AROCs 0.913 with 95% CI 0.855 to 0.970 for superior sector, AROCs 0.938 with 95% CI 0.894 to 0.982 for inferior sector, and AROCs 0.895 with 95% CI 0.832 to 0.957 for average).

Conclusion

Our study demonstrated that DL models based on pre-trained CNN are capable of identifying glaucoma with high sensitivity and specificity based on SD-OCT pRNFL images.

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References

  1. Quigley HA (2011) Glaucoma. Lancet 377:1367–1377

    Article  Google Scholar 

  2. Weinreb RN, Aung T, Medeiros FA (2014) The pathophysiology and treatment of glaucoma: a review. JAMA 311:1901–1911

    Article  Google Scholar 

  3. Sung KR, Kim JS, Wollstein G et al (2011) Imaging of the retinal nerve fibre layer with spectral domain optical coherence tomography for glaucoma diagnosis. Br J Ophthalmol 95:909–914. https://doi.org/10.1136/bjo.2010.186924

    Article  PubMed  Google Scholar 

  4. Medeiros FA, Zangwill LM, Bowd C et al (2005) Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography. Am J Ophthalmol 139:44–55

    Article  Google Scholar 

  5. Leung CK, Cheung CY, Weinreb RN et al (2009) Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: a variability and diagnostic performance study. Ophthalmology 116:1257–1263

    Article  Google Scholar 

  6. Knight OJ, Chang RT, Feuer WJ et al (2009) Comparison of retinal nerve fiber layer measurements using time domain and spectral domain optical coherent tomography. Ophthalmology 116:1271–1277

    Article  Google Scholar 

  7. Asaoka R, Murata H, Hirasawa K et al (2018) Using deep learning and transform learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images. Am J Ophthalmol. https://doi.org/10.1016/j.ajo.2018.10.007 (1879-1891 (Electronic))

    Article  Google Scholar 

  8. Gulshan V, Peng L, Coram M et al (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402

    Article  Google Scholar 

  9. Shen W, Zhou M, Yang F et al (2015) Multi-scale convolutional neural networks for lung nodule classification. Info Process Med Imaging 24:588–599

  10. Lakhani P, Sundaram B (2017) Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284(2):574–582. https://doi.org/10.1148/radiol.2017162326

    Article  PubMed  Google Scholar 

  11. Le MH, Chen J, Wang L et al (2017) Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks. Phys Med Biol 62(16):6491–6514. https://doi.org/10.1088/1361-6560/aa7731

    Article  Google Scholar 

  12. Asaoka R, Murata H, Iwase A et al (2016) Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier. Ophthalmology 123(9):1974–1980. https://doi.org/10.1016/j.ophtha.2016.05.029

    Article  PubMed  Google Scholar 

  13. Rasband WS. ImageJ, US. National Institutes of Health, Bethesda, Maryland, USA. http://imagej.nih.gov/ij/. Accessed 2 Oct 2019

  14. Shabana N, Aquino MC, See J et al (2012) Quantitative evaluation of anterior chamber parameters using anterior segment optical coherence tomography in primary angle closure mechanisms. Clin Exp Ophthalmol 40(8):792–801. https://doi.org/10.1111/j.1442-9071.2012.02805.x

    Article  PubMed  Google Scholar 

  15. Zheng C, de Leon JM, Cheung CY et al (2016) Determinants of pupil diameters and pupil dynamics in an adult Chinese population. Graefes Arch Clin Exp Ophthalmol 254(5):929–936. https://doi.org/10.1007/s00417-016-3272-7

    Article  PubMed  Google Scholar 

  16. Chen B, Gao E, Chen H et al (2016) Profile and determinants of retinal optical intensity in normal eyes with spectral domain optical coherence tomography. PLoS One 11(2):1–16. https://doi.org/10.1371/journal.pone.0148183

    Article  CAS  Google Scholar 

  17. Russakovsky O, Deng J, Su H et al (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115.3(2015):211–252. https://doi.org/10.1007/s11263-015-0816-y

    Article  Google Scholar 

  18. Szegedy C, Vanhoucke V, Ioffe S, et al. (2016) Rethinking the inception architecture for computer vision. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2818-2826.doi:https://doi.org/10.1109/CVPR.2016.308

  19. Zhou B, Khosla A, Lapedriza A, et al. (2015) Learning deep features for discriminative localization. CVPR'16 (arXiv:1512.04150, 2015)

  20. DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3):837–845

    Article  CAS  Google Scholar 

  21. Rao HL, Zangwill LM, Weinreb RN et al (2010) Comparison of different spectral domain optical coherence tomography scanning areas for glaucoma diagnosis. Ophthalmology 117(9):1692–1699.e1. https://doi.org/10.1016/j.ophtha.2010.01.031

    Article  PubMed  Google Scholar 

  22. Seong M, Sung KR, Choi EH et al (2010) Macular and peripapillary retinal nerve fiber layer measurements by spectral domain optical coherence tomography in normal-tension glaucoma. Investig Ophthalmol Vis Sci 51(3):1446–1452. https://doi.org/10.1167/iovs.09-4258

    Article  Google Scholar 

  23. Wang X, Peng Y, Lu L, et al. (2017) ChestX-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 3462-3471. https://doi.org/10.1109/CVPR.2017.369

  24. Yosinski J, Clune J, Nguyen A, et al. (2015) Understanding neural networks through deep visualization. arXiv preprint. https://arxiv.org/abs/1506.06579. Published June 22, 2015. Accessed Augest 12, 2019

  25. Vermeer KA, van der Schoot J, Lemij HG et al (2012) RPE-normalized RNFL attenuation coefficient maps derived from volumetric OCT imaging for glaucoma assessment. Investig Ophthalmol Vis Sci 53(10):6102–6108. https://doi.org/10.1167/iovs.12-9933

    Article  Google Scholar 

  26. Xu H, Zhai R, Zong Y et al (2018) Comparison of retinal microvascular changes in eyes with high-tension glaucoma or normal-tension glaucoma: a quantitative optic coherence tomography angiographic study. Graefes Arch Clin Exp Ophthcalmol 256(6):1179–1186

    Article  Google Scholar 

  27. Rolle T, Briamonte C, Curto D et al (2011) Ganglion cell complex and retinal nerve fiber layer measured by fourier-domain optical coherence tomography for early detection of structural damage in patients with preperimetric glaucoma. Clin Ophthalmol 5:961–969. https://doi.org/10.2147/OPTH.S20249

    Article  PubMed  PubMed Central  Google Scholar 

  28. Kim S Y , Park H Y L , Park C K . (2012) The effects of peripapillary atrophy on the diagnostic ability of stratus and cirrus oct in the analysis of optic nerve head parameters and disc size [J]. Invest Ophthalmol Vis Sci 53(8)

    Article  Google Scholar 

Download references

Funding

This study was funded by the National Natural Science Foundation of China (81371010) and Clinical Research Funds of Shantou University Medical College (2014).

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Authors and Affiliations

  1. Department of Ophthalmology, Shanghai Children’s Hospital, Shanghai Jiao Tong University, Shanghai, China

    Ce Zheng & Tong Qiao

  2. Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical College, Shantou, 515000, Guangdong, China

    Ce Zheng, Xiaolin Xie, Binyao Chen, Jianling Yang & Mingzhi Zhang

  3. Department of Electronic Engineering, Shantou University, Shantou, Guangdong, China

    Longtao Huang, Jiewei Lu & Zhun Fan

Authors
  1. Ce Zheng
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  2. Xiaolin Xie
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  3. Longtao Huang
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  4. Binyao Chen
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  5. Jianling Yang
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  6. Jiewei Lu
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  7. Tong Qiao
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  8. Zhun Fan
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  9. Mingzhi Zhang
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Contributions

CZ, MZZ, and ZF contributed towards the conception and design, drafted the manuscript, and approved the final version. CZ, JWL, and LTH developed the artificial intelligence system evaluated in this study. XLX, JLY, TQ and BYC gathered, cleaned, and organized the data.

Corresponding author

Correspondence to Mingzhi Zhang.

Ethics declarations

This study was conducted according to the tenets of the Declaration of Helsinki and had the approval of the institutional review board.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in this study were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Zheng, C., Xie, X., Huang, L. et al. Detecting glaucoma based on spectral domain optical coherence tomography imaging of peripapillary retinal nerve fiber layer: a comparison study between hand-crafted features and deep learning model. Graefes Arch Clin Exp Ophthalmol 258, 577–585 (2020). https://doi.org/10.1007/s00417-019-04543-4

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  • DOI: https://doi.org/10.1007/s00417-019-04543-4

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