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Items: 1 to 20 of 101

1.

Joint retina segmentation and classification for early glaucoma diagnosis.

Wang J, Wang Z, Li F, Qu G, Qiao Y, Lv H, Zhang X.

Biomed Opt Express. 2019 Apr 30;10(5):2639-2656. doi: 10.1364/BOE.10.002639. eCollection 2019 May 1.

2.

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.

3.

A supervised joint multi-layer segmentation framework for retinal optical coherence tomography images using conditional random field.

Chakravarty A, Sivaswamy J.

Comput Methods Programs Biomed. 2018 Oct;165:235-250. doi: 10.1016/j.cmpb.2018.09.004. Epub 2018 Sep 5.

PMID:
30337078
4.

Beyond Retinal Layers: A Deep Voting Model for Automated Geographic Atrophy Segmentation in SD-OCT Images.

Ji Z, Chen Q, Niu S, Leng T, Rubin DL.

Transl Vis Sci Technol. 2018 Jan 2;7(1):1. doi: 10.1167/tvst.7.1.1. eCollection 2018 Jan.

5.

Analysis of inner and outer retinal layers using spectral domain optical coherence tomography automated segmentation software in ocular hypertensive and glaucoma patients.

Cifuentes-Canorea P, Ruiz-Medrano J, Gutierrez-Bonet R, Peña-Garcia P, Saenz-Frances F, Garcia-Feijoo J, Martinez-de-la-Casa JM.

PLoS One. 2018 Apr 19;13(4):e0196112. doi: 10.1371/journal.pone.0196112. eCollection 2018.

6.

Retinal nerve fiber layer and macular inner retina measurements by spectral domain optical coherence tomograph in Indian eyes with early glaucoma.

Rao HL, Babu JG, Addepalli UK, Senthil S, Garudadri CS.

Eye (Lond). 2012 Jan;26(1):133-9. doi: 10.1038/eye.2011.277. Epub 2011 Nov 11.

7.

Fully automated detection of retinal disorders by image-based deep learning.

Li F, Chen H, Liu Z, Zhang X, Wu Z.

Graefes Arch Clin Exp Ophthalmol. 2019 Mar;257(3):495-505. doi: 10.1007/s00417-018-04224-8. Epub 2019 Jan 4.

PMID:
30610422
8.

Combining information from 3 anatomic regions in the diagnosis of glaucoma with time-domain optical coherence tomography.

Wang M, Lu AT, Varma R, Schuman JS, Greenfield DS, Huang D; Advanced Imaging for Glaucoma Study Group.

J Glaucoma. 2014 Mar;23(3):129-35. doi: 10.1097/IJG.0b013e318264b941.

9.

Evaluation of retinal nerve fiber layer, optic nerve head, and macular thickness measurements for glaucoma detection using optical coherence tomography.

Medeiros FA, Zangwill LM, Bowd C, Vessani RM, Susanna R Jr, Weinreb RN.

Am J Ophthalmol. 2005 Jan;139(1):44-55.

PMID:
15652827
10.

Diagnostic Ability of Wide-field Retinal Nerve Fiber Layer Maps Using Swept-Source Optical Coherence Tomography for Detection of Preperimetric and Early Perimetric Glaucoma.

Lee WJ, Na KI, Kim YK, Jeoung JW, Park KH.

J Glaucoma. 2017 Jun;26(6):577-585. doi: 10.1097/IJG.0000000000000662.

PMID:
28368998
11.

Analyzing the impact of glaucoma on the macular architecture using spectral-domain optical coherence tomography.

Unterlauft JD, Rehak M, Böhm MRR, Rauscher FG.

PLoS One. 2018 Dec 31;13(12):e0209610. doi: 10.1371/journal.pone.0209610. eCollection 2018.

12.

A formula to predict spectral domain optical coherence tomography (OCT) retinal nerve fiber layer measurements based on time domain OCT measurements.

Lee KH, Kang MG, Lim H, Kim CY, Kim NR.

Korean J Ophthalmol. 2012 Oct;26(5):369-77. doi: 10.3341/kjo.2012.26.5.369. Epub 2012 Sep 24.

13.

Early glaucoma involves both deep local, and shallow widespread, retinal nerve fiber damage of the macular region.

Hood DC, Slobodnick A, Raza AS, de Moraes CG, Teng CC, Ritch R.

Invest Ophthalmol Vis Sci. 2014 Feb 3;55(2):632-49. doi: 10.1167/iovs.13-13130.

14.

Effects of Circumpapillary Retinal Nerve Fiber Layer Segmentation Error Correction on Glaucoma Diagnosis in Myopic Eyes.

Suwan Y, Rettig S, Park SC, Tantraworasin A, Geyman LS, Effert K, Silva L, Jarukasetphorn R, Ritch R.

J Glaucoma. 2018 Nov;27(11):971-975. doi: 10.1097/IJG.0000000000001054.

PMID:
30113513
15.

Diagnostic accuracy of macular ganglion cell-inner plexiform layer thickness for glaucoma detection in a population-based study: Comparison with optic nerve head imaging parameters.

Koh V, Tham YC, Cheung CY, Mani B, Wong TY, Aung T, Cheng CY.

PLoS One. 2018 Jun 26;13(6):e0199134. doi: 10.1371/journal.pone.0199134. eCollection 2018.

16.

Macular ganglion cell layer imaging in preperimetric glaucoma with speckle noise-reduced spectral domain optical coherence tomography.

Nakano N, Hangai M, Nakanishi H, Mori S, Nukada M, Kotera Y, Ikeda HO, Nakamura H, Nonaka A, Yoshimura N.

Ophthalmology. 2011 Dec;118(12):2414-26. doi: 10.1016/j.ophtha.2011.06.015. Epub 2011 Sep 15.

17.

Diagnostic Accuracy of Spectralis SD OCT Automated Macular Layers Segmentation to Discriminate Normal from Early Glaucomatous Eyes.

Pazos M, Dyrda AA, Biarnés M, Gómez A, Martín C, Mora C, Fatti G, Antón A.

Ophthalmology. 2017 Aug;124(8):1218-1228. doi: 10.1016/j.ophtha.2017.03.044. Epub 2017 Apr 29.

PMID:
28461015
18.

Prevalence and Associated Factors of Segmentation Errors in the Peripapillary Retinal Nerve Fiber Layer and Macular Ganglion Cell Complex in Spectral-domain Optical Coherence Tomography Images.

Miki A, Kumoi M, Usui S, Endo T, Kawashima R, Morimoto T, Matsushita K, Fujikado T, Nishida K.

J Glaucoma. 2017 Nov;26(11):995-1000. doi: 10.1097/IJG.0000000000000771.

PMID:
28858152
19.

Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography: analysis of the retinal nerve fiber layer map for glaucoma detection.

Leung CK, Lam S, Weinreb RN, Liu S, Ye C, Liu L, He J, Lai GW, Li T, Lam DS.

Ophthalmology. 2010 Sep;117(9):1684-91. doi: 10.1016/j.ophtha.2010.01.026. Epub 2010 Jul 21.

PMID:
20663563
20.

Retinal Nerve Fiber Layer Segmentation on FD-OCT Scans of Normal Subjects and Glaucoma Patients.

Mayer MA, Hornegger J, Mardin CY, Tornow RP.

Biomed Opt Express. 2010 Nov 8;1(5):1358-1383.

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