Sommer, A. et al. Evaluation of nerve fiber layer assessment. Arch. Ophthalmol. 102, 1766–1771 (1984).
O’Neill, E. C. et al. The optic nerve head in acquired optic neuropathies. Nat. Rev. Neurol. 6, 221–236 (2010).
Weinreb, R. N. et al. Primary open-angle glaucoma. Nat. Rev. Dis. Primers 2, 16067 (2016).
Hood, D. C. Improving our understanding, and detection, of glaucomatous damage: an approach based upon optical coherence tomography (OCT). Prog. Retin. Eye Res. 57, 46–75 (2017).
Micieli, J. A., Newman, N. J. & Biousse, V. The role of optical coherence tomography in the evaluation of compressive optic neuropathies. Curr. Opin. Neurol. 32, 115–123 (2019).
Mutlu, U. et al. Association of retinal neurodegeneration on optical coherence tomography with dementia: a population-based study. JAMA Neurol. 75, 1256–1263 (2018).
Petzold, A. et al. Retinal layer segmentation in multiple sclerosis: a systematic review and meta-analysis. Lancet Neurol. 16, 797–812 (2017).
Ahn, J. et al. Retinal thinning associates with nigral dopaminergic loss in de novo Parkinson disease. Neurology 91, e1003–e1012 (2018).
Andrade, C. et al. Spectral-domain optical coherence tomography as a potential biomarker in Huntington’s disease. Mov. Disord. 31, 377–383 (2016).
Doustar, J., Torbati, T., Black, K. L., Koronyo, Y. & Koronyo-Hamaoui, M. Optical coherence tomography in Alzheimer’s disease and other neurodegenerative diseases. Front. Neurol. 8, 701 (2017).
Leung, C. K. et al. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: a variability and diagnostic performance study. Ophthalmology 116, 1257–1263 (2009).
Mwanza, J. C. et al. Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma. Invest. Ophthalmol. Vis. Sci. 52, 8323–8329 (2011).
Oddone, F. et al. Macular versus retinal nerve fiber layer parameters for diagnosing manifest glaucoma: a systematic review of diagnostic accuracy studies. Ophthalmology 123, 939–949 (2016).
Leung, C. K. & Lam, A. K. Optical texture analysis of the inner retina. US patent 62/571,559 (2017).
Flaxman, S. R. et al. Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis. Lancet Glob. Health 5, e1221–e1234 (2017).
Cohen, J. F. et al. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open 6, e012799 (2016).
Pons, M. E. et al. Assessment of retinal nerve fiber layer internal reflectivity in eyes with and without glaucoma using optical coherence tomography. Arch. Ophthalmol. 118, 1044–1047 (2000).
Vermeer, K. A., van der Schoot, J., Lemij, H. G. & de Boer, J. F. RPE-normalized RNFL attenuation coefficient maps derived from volumetric OCT imaging for glaucoma assessment. Invest. Ophthalmol. Vis. Sci. 53, 6102–6108 (2012).
Greenfield, D. S. Glaucomatous versus nonglaucomatous optic disc cupping: clinical differentiation. Semin. Ophthalmol. 14, 95–108 (1999).
Mcleod, D. Pathogenesis of optic disc swelling. Br. J. Ophthalmol. 62, 579–580 (1978).
Biswas, S., Lin, C. & Leung, C. K. Evaluation of a myopic normative database for analysis of retinal nerve fiber layer thickness. JAMA Ophthalmol. 134, 1032–1039 (2016).
Knight, O. J. et al. Effect of race, age, and axial length on optic nerve head parameters and retinal nerve fiber layer thickness measured by Cirrus HD-OCT. Arch. Ophthalmol. 130, 312–318 (2012).
Hood, D. C. et al. Details of glaucomatous damage are better seen on OCT en face images than on OCT retinal nerve fiber layer thickness maps. Invest. Ophthalmol. Vis. Sci. 56, 6208–6216 (2015).
Chauhan, B. C., Sharpe, G. P. & Hutchison, D. M. Imaging of the temporal raphe with optical coherence tomography. Ophthalmology 121, 2287–2288 (2014).
Dong, Z. M., Wollstein, G., Wang, B. & Schuman, J. S. Adaptive optics optical coherence tomography in glaucoma. Prog. Retin. Eye Res. 57, 76–88 (2017).
Hood, D. C. et al. Confocal adaptive optics imaging of peripapillary nerve fiber bundles: implications for glaucomatous damage seen on circumpapillary OCT scans. Transl. Vis. Sci. Technol. 4, 12 (2015).
Bae, H. W. et al. Comparison of three types of images for the detection of retinal nerve fiber layer defects. Optom. Vis. Sci. 92, 500–505 (2015).
Neelam, K., Cheung, C. M., Ohno-Matsui, K., Lai, T. Y. & Wong, T. Y. Choroidal neovascularization in pathological myopia. Prog. Retin. Eye Res. 31, 495–525 (2012).
CCRB Clinical Trials Registry, CUHK_CCRB00439. Progressive Lamina Cribrosa Deformation – A Biomarker for Fast Progressors in Glaucoma (The Chinese University of Hong Kong, 2014); https://www2.ccrb.cuhk.edu.hk/registry/public/278
CCRB Clinical Trials Registry, CUHK_CCRB00591. Measurement of the Rates of Retinal Nerve Fiber Layer Thinning to Guide Management of Glaucoma Patients (The Chinese University of Hong Kong, 2014); https://www2.ccrb.cuhk.edu.hk/registry/public/457
ANZCTR, ACTRN12618000453280. Progressive Retinal Nerve Fiber Layer (RNFL) Thinning as a Biomarker to Guide Intraocular Pressure (IOP) Lowering Treatment in Ocular Hypertensives (OHT). (ANZCTR, 2018); https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=373418
Leung, C.K. in Diagnosis of Primary Open Angle Glaucoma (eds Weinreb, R. N., Leung, C. K., Garway-Heath, D. F., Medeiros, F. A. & Liebmann, J.) 1–20 (WGA Consensus Series 10, Kugler, 2016).
Leung, C. K. et al. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: analysis of the retinal nerve fiber layer map for glaucoma detection. Ophthalmology 117, 1684–1691 (2010).
Haralick, R. M., Shanmugam, K. & Dinstein, I. Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3, 610–621 (1973).
Bovik, A. C., Clark, M. & Geisler, W. S. Multichannel texture analysis using localized spatial filters. IEEE Trans. Pattern Anal. Mach. Intell. 12, 55–73 (1990).
Anitha, J. & Peter, J. D. A wavelet based morphological mass detection and classification in mammograms. In International Conference on Machine Vision and Image Processing (MVIP) 25–28 (2012).
Ben Salem, Y. & Nasri, S. Automatic recognition of woven fabrics based on texture and using SVM. Signal Image Video Process. 4, 429–434 (2010).
Kandaswamy, U., Adjeroh, D. A. & Lee, M. C. Efficient texture analysis of SAR imagery. IEEE Trans. Geosci. Remote Sens. 43, 2075–2083 (2005).
Bharati, M. H., Liu, J. J. & MacGregor, J. F. Image texture analysis: methods and comparisons. Chemometr. Intell. Lab. Syst. 72, 57–71 (2004).
Youden, W. J. Index for rating diagnostic tests. Cancer 3, 32–35 (1950).
Yang, Z., Sun, X. & Hardin, J. W. A note on the tests for clustered matched-pair binary data. Biom. J. 52, 638–652 (2010).
Obuchowski, N. A. On the comparison of correlated proportions for clustered data. Stat. Med. 17, 1495–1507 (1998).
Pepe, M. S. Three approaches to regression analysis of receiver operating characteristic curves for continuous test results. Biometrics 54, 124–135 (1998).