Literature DB >> 23349432

Semiautomated segmentation of the choroid in spectral-domain optical coherence tomography volume scans.

Zhihong Hu1, Xiaodong Wu, Yanwei Ouyang, Yanling Ouyang, Srinivas R Sadda.   

Abstract

PURPOSE: Changes in the choroid, in particular its thickness, are believed to be of importance in the pathophysiology of a number of retinal diseases. The purpose of this study was to adapt the graph search algorithm to semiautomatically identify the choroidal layer in spectral-domain optical coherence tomography (SD-OCT) volume scans and compare its performance to manual delineation.
METHODS: A graph-based multistage segmentation approach was used to identify the choroid, defined as the layer between the outer border of the RPE band and the choroid-sclera junction. Thirty randomly chosen macular SD-OCT (1024 × 37 × 496 voxels, Heidelberg Spectralis) volumes were obtained from 20 healthy subjects and 10 subjects with non-neovascular AMD. The positions of the choroidal borders and resultant thickness were compared with consensus manual delineation performed by two graders. For consistency of the statistical analysis, the left eyes were horizontally flipped in the x-direction.
RESULTS: The algorithm-defined position of the outer RPE border and choroid-sclera junction was consistent with the manual delineation, resulting in highly correlated choroidal thickness values with r = 0.91 to 0.93 for the healthy subjects and 0.94 for patients with non-neovascular AMD. Across all cases, the mean and absolute differences between the algorithm and manual segmentation for the outer RPE boundary was -0.74 ± 3.27 μm and 3.15 ± 3.07 μm; and for the choroid-sclera junction was -3.90 ± 15.93 μm and 21.39 ± 10.71 μm.
CONCLUSIONS: Excellent agreement was observed between the algorithm and manual choroidal segmentation in both normal eyes and those with non-neovascular AMD. The choroid was thinner in AMD eyes. Semiautomated choroidal thickness calculation may be useful for large-scale quantitative studies of the choroid.

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Year:  2013        PMID: 23349432     DOI: 10.1167/iovs.12-10578

Source DB:  PubMed          Journal:  Invest Ophthalmol Vis Sci        ISSN: 0146-0404            Impact factor:   4.799


  20 in total

1.  Multi-penalty conditional random field approach to super-resolved reconstruction of optical coherence tomography images.

Authors:  Ameneh Boroomand; Alexander Wong; Edward Li; Daniel S Cho; Betty Ni; Kostandinka Bizheva
Journal:  Biomed Opt Express       Date:  2013-09-06       Impact factor: 3.732

2.  Validity of Automated Choroidal Segmentation in SS-OCT and SD-OCT.

Authors:  Li Zhang; Gabriëlle H S Buitendijk; Kyungmoo Lee; Milan Sonka; Henriët Springelkamp; Albert Hofman; Johannes R Vingerling; Robert F Mullins; Caroline C W Klaver; Michael D Abràmoff
Journal:  Invest Ophthalmol Vis Sci       Date:  2015-05       Impact factor: 4.799

3.  Semiautomated segmentation and analysis of retinal layers in three-dimensional spectral-domain optical coherence tomography images of patients with atrophic age-related macular degeneration.

Authors:  Zhihong Hu; Yue Shi; Kiran Nandanan; Srinivas R Sadda
Journal:  Neurophotonics       Date:  2017-02-06       Impact factor: 3.593

4.  Three-dimensional automated choroidal volume assessment on standard spectral-domain optical coherence tomography and correlation with the level of diabetic macular edema.

Authors:  Bianca S Gerendas; Sebastian M Waldstein; Christian Simader; Gabor Deak; Bilal Hajnajeeb; Li Zhang; Hrvoje Bogunovic; Michael D Abramoff; Michael Kundi; Milan Sonka; Ursula Schmidt-Erfurth
Journal:  Am J Ophthalmol       Date:  2014-08-12       Impact factor: 5.258

5.  Automatic segmentation of choroidal thickness in optical coherence tomography.

Authors:  David Alonso-Caneiro; Scott A Read; Michael J Collins
Journal:  Biomed Opt Express       Date:  2013-11-11       Impact factor: 3.732

6.  Tissue thickness calculation in ocular optical coherence tomography.

Authors:  David Alonso-Caneiro; Scott A Read; Stephen J Vincent; Michael J Collins; Maciej Wojtkowski
Journal:  Biomed Opt Express       Date:  2016-01-21       Impact factor: 3.732

7.  An automated method for choroidal thickness measurement from Enhanced Depth Imaging Optical Coherence Tomography images.

Authors:  Md Akter Hussain; Alauddin Bhuiyan; Hiroshi Ishikawa; R Theodore Smith; Joel S Schuman; Ramamohanrao Kotagiri
Journal:  Comput Med Imaging Graph       Date:  2018-01-06       Impact factor: 4.790

8.  A novel and faster method of manual grading to measure choroidal thickness using optical coherence tomography.

Authors:  K X Cheong; L W Lim; K Z Li; C S Tan
Journal:  Eye (Lond)       Date:  2017-10-20       Impact factor: 3.775

9.  Automated segmentation of the choroid in EDI-OCT images with retinal pathology using convolution neural networks.

Authors:  Min Chen; Jiancong Wang; Ipek Oguz; Brian L VanderBeek; James C Gee
Journal:  Fetal Infant Ophthalmic Med Image Anal (2017)       Date:  2017-09-09

10.  Validation of Macular Choroidal Thickness Measurements from Automated SD-OCT Image Segmentation.

Authors:  Michael D Twa; Krystal L Schulle; Stephanie J Chiu; Sina Farsiu; David A Berntsen
Journal:  Optom Vis Sci       Date:  2016-11       Impact factor: 1.973

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