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Items: 1 to 20 of 109

1.

Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation.

Chiu SJ, Li XT, Nicholas P, Toth CA, Izatt JA, Farsiu S.

Opt Express. 2010 Aug 30;18(18):19413-28. doi: 10.1364/OE.18.019413.

2.

Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming.

Larocca F, Chiu SJ, McNabb RP, Kuo AN, Izatt JA, Farsiu S.

Biomed Opt Express. 2011 Jun 1;2(6):1524-38. doi: 10.1364/BOE.2.001524.

3.

Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology.

Srinivasan PP, Heflin SJ, Izatt JA, Arshavsky VY, Farsiu S.

Biomed Opt Express. 2014 Jan 7;5(2):348-65. doi: 10.1364/BOE.5.000348.

4.

User-guided segmentation for volumetric retinal optical coherence tomography images.

Yin X, Chao JR, Wang RK.

J Biomed Opt. 2014 Aug;19(8):086020. doi: 10.1117/1.JBO.19.8.086020.

5.

Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region.

Tian J, Varga B, Somfai GM, Lee WH, Smiddy WE, DeBuc DC.

PLoS One. 2015 Aug 10;10(8):e0133908. doi: 10.1371/journal.pone.0133908.

6.

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.

PMID:
26307452
7.

Automated layer segmentation of optical coherence tomography images.

Lu S, Cheung CY, Liu J, Lim JH, Leung CK, Wong TY.

IEEE Trans Biomed Eng. 2010 Oct;57(10):2605-8. doi: 10.1109/TBME.2010.2055057.

PMID:
20595078
8.

Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints.

Dufour PA, Ceklic L, Abdillahi H, Schröder S, De Dzanet S, Wolf-Schnurrbusch U, Kowal J.

IEEE Trans Med Imaging. 2013 Mar;32(3):531-43. doi: 10.1109/TMI.2012.2225152.

PMID:
23086520
9.

Automated layer segmentation of macular OCT images using dual-scale gradient information.

Yang Q, Reisman CA, Wang Z, Fukuma Y, Hangai M, Yoshimura N, Tomidokoro A, Araie M, Raza AS, Hood DC, Chan K.

Opt Express. 2010 Sep 27;18(20):21293-307. doi: 10.1364/OE.18.021293.

10.

[Automated segmentation of retina layer structures on optical coherence tomography].

Gao Y, Li Y, Wang L, Zhang M.

Zhongguo Yi Liao Qi Xie Za Zhi. 2014 Mar;38(2):94-7, 101. Chinese.

PMID:
24941769
11.

Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach.

Yazdanpanah A, Hamarneh G, Smith BR, Sarunic MV.

IEEE Trans Med Imaging. 2011 Feb;30(2):484-96. doi: 10.1109/TMI.2010.2087390.

PMID:
20952331
12.

Order preserving and shape prior constrained intra-retinal layer segmentation in optical coherence tomography.

Rathke F, Schmidt S, Schnörr C.

Med Image Comput Comput Assist Interv. 2011;14(Pt 3):370-7.

PMID:
22003721
13.

Fully automatic software for retinal thickness in eyes with diabetic macular edema from images acquired by cirrus and spectralis systems.

Lee JY, Chiu SJ, Srinivasan PP, Izatt JA, Toth CA, Farsiu S, Jaffe GJ.

Invest Ophthalmol Vis Sci. 2013 Nov 15;54(12):7595-602. doi: 10.1167/iovs.13-11762.

14.

Development of a semi-automatic segmentation method for retinal OCT images tested in patients with diabetic macular edema.

Huang Y, Danis RP, Pak JW, Luo S, White J, Zhang X, Narkar A, Domalpally A.

PLoS One. 2013 Dec 26;8(12):e82922. doi: 10.1371/journal.pone.0082922.

15.

Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema.

Chiu SJ, Allingham MJ, Mettu PS, Cousins SW, Izatt JA, Farsiu S.

Biomed Opt Express. 2015 Mar 9;6(4):1172-94. doi: 10.1364/BOE.6.001172.

16.

Automated segmentation of intramacular layers in Fourier domain optical coherence tomography structural images from normal subjects.

Zhang X, Yousefi S, An L, Wang RK.

J Biomed Opt. 2012 Apr;17(4):046011. doi: 10.1117/1.JBO.17.4.046011.

17.

Segmentation of choroidal boundary in enhanced depth imaging OCTs using a multiresolution texture based modeling in graph cuts.

Danesh H, Kafieh R, Rabbani H, Hajizadeh F.

Comput Math Methods Med. 2014;2014:479268. doi: 10.1155/2014/479268.

18.

Profile and Determinants of Retinal Optical Intensity in Normal Eyes with Spectral Domain Optical Coherence Tomography.

Chen B, Gao E, Chen H, Yang J, Shi F, Zheng C, Zhu W, Xiang D, Chen X, Zhang M.

PLoS One. 2016 Feb 10;11(2):e0148183. doi: 10.1371/journal.pone.0148183.

19.

Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map.

Kafieh R, Rabbani H, Abramoff MD, Sonka M.

Med Image Anal. 2013 Dec;17(8):907-28. doi: 10.1016/j.media.2013.05.006.

20.

Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images.

Garvin MK, Abràmoff MD, Wu X, Russell SR, Burns TL, Sonka M.

IEEE Trans Med Imaging. 2009 Sep;28(9):1436-47. doi: 10.1109/TMI.2009.2016958.

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