Literature DB >> 16689260

Automatic recovery of the optic nervehead geometry in optical coherence tomography.

Kim L Boyer1, Artemas Herzog, Cynthia Roberts.   

Abstract

Optical coherence tomography (OCT) uses retroreflected light to provide micrometer-resolution, cross-sectional scans of biological tissues. OCT's first application was in ophthalmic imaging where it has proven particularly useful in diagnosing, monitoring, and studying glaucoma. Diagnosing glaucoma is difficult and it often goes undetected until significant damage to the subject's visual field has occurred. As glaucoma progresses, neural tissue dies, the nerve fiber layer thins, and the cup-to-disk ratio increases. Unfortunately, most current measurement techniques are subjective and inherently unreliable, making it difficult to monitor small changes in the nervehead geometry. To our knowledge, this paper presents the first published results on optic nervehead segmentation and geometric characterization from OCT data. We develop complete, autonomous algorithms based on a parabolic model of cup geometry and an extension of the Markov model introduced by Koozekanani, et al. to segment the retinal-nervehead surface, identify the choroid-nervehead boundary, and identify the extent of the optic cup. We present thorough experimental results from both normal and pathological eyes, and compare our results against those of an experienced, expert ophthalmologist, reporting a correlation coefficient for cup diameter above 0.8 and above 0.9 for the disk diameter.

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Year:  2006        PMID: 16689260     DOI: 10.1109/TMI.2006.871417

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  13 in total

1.  3-D histomorphometry of the normal and early glaucomatous monkey optic nerve head: lamina cribrosa and peripapillary scleral position and thickness.

Authors:  Hongli Yang; J Crawford Downs; Christopher Girkin; Lisandro Sakata; Anthony Bellezza; Hilary Thompson; Claude F Burgoyne
Journal:  Invest Ophthalmol Vis Sci       Date:  2007-10       Impact factor: 4.799

2.  Optical coherence tomography image denoising using a generative adversarial network with speckle modulation.

Authors:  Zhao Dong; Guoyan Liu; Guangming Ni; Jason Jerwick; Lian Duan; Chao Zhou
Journal:  J Biophotonics       Date:  2020-02-03       Impact factor: 3.207

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

Authors:  Raheleh Kafieh; Hossein Rabbani; Michael D Abramoff; Milan Sonka
Journal:  Med Image Anal       Date:  2013-06-11       Impact factor: 8.545

4.  Automated 3D segmentation of multiple surfaces with a shared hole: segmentation of the neural canal opening in SD-OCT volumes.

Authors:  Bhavna J Antony; Mohammed S Miri; Michael D Abràmoff; Young H Kwon; Mona K Garvin
Journal:  Med Image Comput Comput Assist Interv       Date:  2014

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

Authors:  Ahmet Murat Bagci; Mahnaz Shahidi; Rashid Ansari; Michael Blair; Norman Paul Blair; Ruth Zelkha
Journal:  Am J Ophthalmol       Date:  2008-08-15       Impact factor: 5.258

6.  Three-dimensional histomorphometry of the normal and early glaucomatous monkey optic nerve head: neural canal and subarachnoid space architecture.

Authors:  J Crawford Downs; Hongli Yang; Christopher Girkin; Lisandro Sakata; Anthony Bellezza; Hilary Thompson; Claude F Burgoyne
Journal:  Invest Ophthalmol Vis Sci       Date:  2007-07       Impact factor: 4.799

7.  Curvature correction of retinal OCTs using graph-based geometry detection.

Authors:  Raheleh Kafieh; Hossein Rabbani; Michael D Abramoff; Milan Sonka
Journal:  Phys Med Biol       Date:  2013-04-11       Impact factor: 3.609

8.  Statistical Models of Signal and Noise and Fundamental Limits of Segmentation Accuracy in Retinal Optical Coherence Tomography.

Authors:  Theodore B Dubose; David Cunefare; Elijah Cole; Peyman Milanfar; Joseph A Izatt; Sina Farsiu
Journal:  IEEE Trans Med Imaging       Date:  2017-11-13       Impact factor: 10.048

9.  Thickness mapping of eleven retinal layers segmented using the diffusion maps method in normal eyes.

Authors:  Raheleh Kafieh; Hossein Rabbani; Fedra Hajizadeh; Michael D Abramoff; Milan Sonka
Journal:  J Ophthalmol       Date:  2015-04-19       Impact factor: 1.909

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

Authors:  Jing Tian; Boglárka Varga; Gábor Márk Somfai; Wen-Hsiang Lee; William E Smiddy; Delia Cabrera DeBuc
Journal:  PLoS One       Date:  2015-08-10       Impact factor: 3.240

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