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Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images.

Publication ,  Journal Article
Chiu, SJ; Izatt, JA; O'Connell, RV; Winter, KP; Toth, CA; Farsiu, S
Published in: Invest Ophthalmol Vis Sci
January 5, 2012

PURPOSE: To automatically segment retinal spectral domain optical coherence tomography (SD-OCT) images of eyes with age-related macular degeneration (AMD) and various levels of image quality to advance the study of retinal pigment epithelium (RPE)+drusen complex (RPEDC) volume changes indicative of AMD progression. METHODS: A general segmentation framework based on graph theory and dynamic programming was used to segment three retinal boundaries in SD-OCT images of eyes with drusen and geographic atrophy (GA). A validation study for eyes with nonneovascular AMD was conducted, forming subgroups based on scan quality and presence of GA. To test for accuracy, the layer thickness results from two certified graders were compared against automatic segmentation results for 220 B-scans across 20 patients. For reproducibility, automatic layer volumes were compared that were generated from 0° versus 90° scans in five volumes with drusen. RESULTS: The mean differences in the measured thicknesses of the total retina and RPEDC layers were 4.2 ± 2.8 and 3.2 ± 2.6 μm for automatic versus manual segmentation. When the 0° and 90° datasets were compared, the mean differences in the calculated total retina and RPEDC volumes were 0.28% ± 0.28% and 1.60% ± 1.57%, respectively. The average segmentation time per image was 1.7 seconds automatically versus 3.5 minutes manually. CONCLUSIONS: The automatic algorithm accurately and reproducibly segmented three retinal boundaries in images containing drusen and GA. This automatic approach can reduce time and labor costs and yield objective measurements that potentially reveal quantitative RPE changes in longitudinal clinical AMD studies. (ClinicalTrials.gov number, NCT00734487.).

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Published In

Invest Ophthalmol Vis Sci

DOI

EISSN

1552-5783

Publication Date

January 5, 2012

Volume

53

Issue

1

Start / End Page

53 / 61

Location

United States

Related Subject Headings

  • Tomography, Optical Coherence
  • Retinal Pigment Epithelium
  • Retinal Drusen
  • Reproducibility of Results
  • Ophthalmology & Optometry
  • Macular Degeneration
  • Humans
  • Geographic Atrophy
  • Disease Progression
  • Algorithms
 

Citation

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Chiu, S. J., Izatt, J. A., O’Connell, R. V., Winter, K. P., Toth, C. A., & Farsiu, S. (2012). Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images. Invest Ophthalmol Vis Sci, 53(1), 53–61. https://doi.org/10.1167/iovs.11-7640
Chiu, Stephanie J., Joseph A. Izatt, Rachelle V. O’Connell, Katrina P. Winter, Cynthia A. Toth, and Sina Farsiu. “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images.Invest Ophthalmol Vis Sci 53, no. 1 (January 5, 2012): 53–61. https://doi.org/10.1167/iovs.11-7640.
Chiu SJ, Izatt JA, O’Connell RV, Winter KP, Toth CA, Farsiu S. Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images. Invest Ophthalmol Vis Sci. 2012 Jan 5;53(1):53–61.
Chiu, Stephanie J., et al. “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images.Invest Ophthalmol Vis Sci, vol. 53, no. 1, Jan. 2012, pp. 53–61. Pubmed, doi:10.1167/iovs.11-7640.
Chiu SJ, Izatt JA, O’Connell RV, Winter KP, Toth CA, Farsiu S. Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images. Invest Ophthalmol Vis Sci. 2012 Jan 5;53(1):53–61.

Published In

Invest Ophthalmol Vis Sci

DOI

EISSN

1552-5783

Publication Date

January 5, 2012

Volume

53

Issue

1

Start / End Page

53 / 61

Location

United States

Related Subject Headings

  • Tomography, Optical Coherence
  • Retinal Pigment Epithelium
  • Retinal Drusen
  • Reproducibility of Results
  • Ophthalmology & Optometry
  • Macular Degeneration
  • Humans
  • Geographic Atrophy
  • Disease Progression
  • Algorithms