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Items: 1 to 20 of 116

1.

Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images.

Vermeer KA, van der Schoot J, Lemij HG, de Boer JF.

Biomed Opt Express. 2011 Jun 1;2(6):1743-56. doi: 10.1364/BOE.2.001743. Epub 2011 May 27.

2.

Comparison of Spectralis-OCT, GDxVCC and GDxECC in assessing retinal nerve fiber layer (RNFL) in glaucomatous patients.

Schallenberg M, Dekowski D, Kremmer S, Selbach JM, Steuhl KP.

Graefes Arch Clin Exp Ophthalmol. 2013 May;251(5):1343-53. doi: 10.1007/s00417-012-2219-x. Epub 2012 Dec 19.

PMID:
23250480
3.

In vivo characterization of ischemic retina in diabetic retinopathy.

Reznicek L, Kernt M, Haritoglou C, Kampik A, Ulbig M, Neubauer AS.

Clin Ophthalmol. 2010 Dec 30;5:31-5. doi: 10.2147/OPTH.S13850.

4.

Thickness mapping of retinal layers by spectral-domain optical coherence tomography.

Loduca AL, Zhang C, Zelkha R, Shahidi M.

Am J Ophthalmol. 2010 Dec;150(6):849-55. doi: 10.1016/j.ajo.2010.06.034. Epub 2010 Oct 16.

5.

Thickness profiles of retinal layers by optical coherence tomography image segmentation.

Bagci AM, Shahidi M, Ansari R, Blair M, Blair NP, Zelkha R.

Am J Ophthalmol. 2008 Nov;146(5):679-87. doi: 10.1016/j.ajo.2008.06.010. Epub 2008 Aug 15.

6.

Macular segmentation with optical coherence tomography.

Ishikawa H, Stein DM, Wollstein G, Beaton S, Fujimoto JG, Schuman JS.

Invest Ophthalmol Vis Sci. 2005 Jun;46(6):2012-7.

7.

Retinal layer segmentation in patients with multiple sclerosis using spectral domain optical coherence tomography.

Garcia-Martin E, Polo V, Larrosa JM, Marques ML, Herrero R, Martin J, Ara JR, Fernandez J, Pablo LE.

Ophthalmology. 2014 Feb;121(2):573-9. doi: 10.1016/j.ophtha.2013.09.035. Epub 2013 Nov 20.

PMID:
24268855
8.

A novel automated segmentation method for retinal layers in OCT images proves retinal degeneration after optic neuritis.

Droby A, Panagoulias M, Albrecht P, Reuter E, Duning T, Hildebrandt A, Wiendl H, Zipp F, Methner A.

Br J Ophthalmol. 2016 Apr;100(4):484-90. doi: 10.1136/bjophthalmol-2014-306015. Epub 2015 Aug 25.

PMID:
26307452
9.

In vivo assessment of retinal neuronal layers in multiple sclerosis with manual and automated optical coherence tomography segmentation techniques.

Seigo MA, Sotirchos ES, Newsome S, Babiarz A, Eckstein C, Ford E, Oakley JD, Syc SB, Frohman TC, Ratchford JN, Balcer LJ, Frohman EM, Calabresi PA, Saidha S.

J Neurol. 2012 Oct;259(10):2119-30. doi: 10.1007/s00415-012-6466-x. Epub 2012 Mar 15.

PMID:
22418995
10.

Comparison of point estimates and average thicknesses of retinal layers measured using manual optical coherence tomography segmentation for quantification of retinal neurodegeneration in multiple sclerosis.

Sotirchos ES, Seigo MA, Calabresi PA, Saidha S.

Curr Eye Res. 2013 Jan;38(1):224-8. doi: 10.3109/02713683.2012.722243. Epub 2012 Sep 6.

PMID:
22954302
11.

Quantitative Analysis of Mouse Retinal Layers Using Automated Segmentation of Spectral Domain Optical Coherence Tomography Images.

Dysli C, Enzmann V, Sznitman R, Zinkernagel MS.

Transl Vis Sci Technol. 2015 Aug 25;4(4):9. eCollection 2015 Aug.

12.

Three-dimensional retinal imaging with high-speed ultrahigh-resolution optical coherence tomography.

Wojtkowski M, Srinivasan V, Fujimoto JG, Ko T, Schuman JS, Kowalczyk A, Duker JS.

Ophthalmology. 2005 Oct;112(10):1734-46.

13.

Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: patterns of retinal nerve fiber layer progression.

Leung CK, Yu M, Weinreb RN, Lai G, Xu G, Lam DS.

Ophthalmology. 2012 Sep;119(9):1858-66. doi: 10.1016/j.ophtha.2012.03.044. Epub 2012 Jun 5.

PMID:
22677426
14.

Morphologic and functional association of retinal layers beneath the epiretinal membrane with spectral-domain optical coherence tomography in eyes without photoreceptor abnormality.

Koo HC, Rhim WI, Lee EK.

Graefes Arch Clin Exp Ophthalmol. 2012 Apr;250(4):491-8. doi: 10.1007/s00417-011-1848-9. Epub 2011 Nov 16.

PMID:
22086759
15.

Changes in cellular structures revealed by ultra-high resolution retinal imaging in optic neuropathies.

Choi SS, Zawadzki RJ, Keltner JL, Werner JS.

Invest Ophthalmol Vis Sci. 2008 May;49(5):2103-19. doi: 10.1167/iovs.07-0980.

16.

Quantitative analysis of the intraretinal layers and optic nerve head using ultra-high resolution optical coherence tomography.

Wang Y, Jiang H, Shen M, Lam BL, DeBuc DC, Ye Y, Li M, Tao A, Shao Y, Wang J.

J Biomed Opt. 2012 Jun;17(6):066013. doi: 10.1117/1.JBO.17.6.066013.

17.

Differential vulnerability of retinal layers to early age-related macular degeneration: evidence by SD-OCT segmentation analysis.

Savastano MC, Minnella AM, Tamburrino A, Giovinco G, Ventre S, Falsini B.

Invest Ophthalmol Vis Sci. 2014 Jan 29;55(1):560-6. doi: 10.1167/iovs.13-12172.

PMID:
24408984
18.

Bayesian machine learning classifiers for combining structural and functional measurements to classify healthy and glaucomatous eyes.

Bowd C, Hao J, Tavares IM, Medeiros FA, Zangwill LM, Lee TW, Sample PA, Weinreb RN, Goldbaum MH.

Invest Ophthalmol Vis Sci. 2008 Mar;49(3):945-53. doi: 10.1167/iovs.07-1083.

PMID:
18326717
19.

Mapping of macular substructures with optical coherence tomography for glaucoma diagnosis.

Tan O, Li G, Lu AT, Varma R, Huang D; Advanced Imaging for Glaucoma Study Group..

Ophthalmology. 2008 Jun;115(6):949-56. Epub 2007 Nov 5.

20.

Automated Segmentation Errors When Using Optical Coherence Tomography to Measure Retinal Nerve Fiber Layer Thickness in Glaucoma.

Mansberger SL, Menda SA, Fortune BA, Gardiner SK, Demirel S.

Am J Ophthalmol. 2017 Feb;174:1-8. doi: 10.1016/j.ajo.2016.10.020. Epub 2016 Nov 4.

PMID:
27818206

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