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Items: 1 to 20 of 36

1.

4D Graph-Based Segmentation for Reproducible and Sensitive Choroid Quantification From Longitudinal OCT Scans.

Oguz I, Abramoff MD, Zhang L, Lee K, Zhang EZ, Sonka M.

Invest Ophthalmol Vis Sci. 2016 Jul 1;57(9):OCT621-OCT630. doi: 10.1167/iovs.15-18924.

2.

Retinal status analysis method based on feature extraction and quantitative grading in OCT images.

Fu D, Tong H, Zheng S, Luo L, Gao F, Minar J.

Biomed Eng Online. 2016 Jul 22;15(1):87. doi: 10.1186/s12938-016-0206-x.

3.

Automated volumetric segmentation of retinal fluid on optical coherence tomography.

Wang J, Zhang M, Pechauer AD, Liu L, Hwang TS, Wilson DJ, Li D, Jia Y.

Biomed Opt Express. 2016 Mar 30;7(4):1577-89. doi: 10.1364/BOE.7.001577. eCollection 2016 Apr 1.

4.

An automated framework for 3D serous pigment epithelium detachment segmentation in SD-OCT images.

Sun Z, Chen H, Shi F, Wang L, Zhu W, Xiang D, Yan C, Li L, Chen X.

Sci Rep. 2016 Feb 22;6:21739. doi: 10.1038/srep21739.

5.

Visual Prognosis of Eyes Recovering From Macular Hole Surgery Through Automated Quantitative Analysis of Spectral-Domain Optical Coherence Tomography (SD-OCT) Scans.

de Sisternes L, Hu J, Rubin DL, Leng T.

Invest Ophthalmol Vis Sci. 2015 Jul;56(8):4631-43. doi: 10.1167/iovs.14-16344.

6.

Automated segmentation of geographic atrophy in fundus autofluorescence images using supervised pixel classification.

Hu Z, Medioni GG, Hernandez M, Sadda SR.

J Med Imaging (Bellingham). 2015 Jan;2(1):014501. doi: 10.1117/1.JMI.2.1.014501. Epub 2015 Jan 12.

7.

RefMoB, a Reflectivity Feature Model-Based Automated Method for Measuring Four Outer Retinal Hyperreflective Bands in Optical Coherence Tomography.

Ross DH, Clark ME, Godara P, Huisingh C, McGwin G, Owsley C, Litts KM, Spaide RF, Sloan KR, Curcio CA.

Invest Ophthalmol Vis Sci. 2015 Jul;56(8):4166-76. doi: 10.1167/iovs.14-15256.

8.

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. eCollection 2015 Apr 1.

9.

Quantitative analysis of retinal layers' optical intensities on 3D optical coherence tomography for central retinal artery occlusion.

Chen H, Chen X, Qiu Z, Xiang D, Chen W, Shi F, Zheng J, Zhu W, Sonka M.

Sci Rep. 2015 Mar 18;5:9269. doi: 10.1038/srep09269.

10.

Relationships of retinal structure and humphrey 24-2 visual field thresholds in patients with glaucoma.

Bogunović H, Kwon YH, Rashid A, Lee K, Critser DB, Garvin MK, Sonka M, Abràmoff MD.

Invest Ophthalmol Vis Sci. 2014 Dec 9;56(1):259-71. doi: 10.1167/iovs.14-15885.

11.

A Method for En Face OCT Imaging of Subretinal Fluid in Age-Related Macular Degeneration.

Mohammad F, Wanek J, Zelkha R, Lim JI, Chen J, Shahidi M.

J Ophthalmol. 2014;2014:720243. doi: 10.1155/2014/720243. Epub 2014 Oct 13.

12.

A survey on computer aided diagnosis for ocular diseases.

Zhang Z, Srivastava R, Liu H, Chen X, Duan L, Kee Wong DW, Kwoh CK, Wong TY, Liu J.

BMC Med Inform Decis Mak. 2014 Aug 31;14:80. doi: 10.1186/1472-6947-14-80. Review.

13.

Multi-surface and multi-field co-segmentation of 3-D retinal optical coherence tomography.

Bogunovic H, Sonka M, Kwon YH, Kemp P, Abramoff MD, Wu X.

IEEE Trans Med Imaging. 2014 Dec;33(12):2242-53. doi: 10.1109/TMI.2014.2336246. Epub 2014 Jul 9.

14.

Retinal imaging as a source of biomarkers for diagnosis, characterization and prognosis of chronic illness or long-term conditions.

MacGillivray TJ, Trucco E, Cameron JR, Dhillon B, Houston JG, van Beek EJ.

Br J Radiol. 2014 Aug;87(1040):20130832. doi: 10.1259/bjr.20130832. Epub 2014 Jun 17. Review.

15.

Optical Coherence Tomography (OCT) Device Independent Intraretinal Layer Segmentation.

Ehnes A, Wenner Y, Friedburg C, Preising MN, Bowl W, Sekundo W, Zu Bexten EM, Stieger K, Lorenz B.

Transl Vis Sci Technol. 2014 Feb 11;3(1):1. eCollection 2014 Feb.

16.

Non-invasive detection of early retinal neuronal degeneration by ultrahigh resolution optical coherence tomography.

Tudor D, Kajić V, Rey S, Erchova I, Považay B, Hofer B, Powell KA, Marshall D, Rosin PL, Drexler W, Morgan JE.

PLoS One. 2014 Apr 28;9(4):e93916. doi: 10.1371/journal.pone.0093916. eCollection 2014.

17.

Quantifying disrupted outer retinal-subretinal layer in SD-OCT images in choroidal neovascularization.

Zhang L, Sonka M, Folk JC, Russell SR, Abràmoff MD.

Invest Ophthalmol Vis Sci. 2014 Apr 11;55(4):2329-35. doi: 10.1167/iovs.13-13048.

18.

Automatic method of analysis of OCT images in assessing the severity degree of glaucoma and the visual field loss.

Koprowski R, Rzendkowski M, Wróbel Z.

Biomed Eng Online. 2014 Feb 14;13:16. doi: 10.1186/1475-925X-13-16.

19.

Automatic analysis of selected choroidal diseases in OCT images of the eye fundus.

Koprowski R, Teper S, Wróbel Z, Wylegala E.

Biomed Eng Online. 2013 Nov 14;12:117. doi: 10.1186/1475-925X-12-117.

20.

A review of algorithms for segmentation of optical coherence tomography from retina.

Kafieh R, Rabbani H, Kermani S.

J Med Signals Sens. 2013 Jan;3(1):45-60. Review.

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