July 2018
Volume 59, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2018
Automated Segmentation of the Anterior Lamina Cribrosa Surface (ALCS) within Optic Nerve Head (ONH) Optical Coherence Tomography (OCT) B-scans
Author Affiliations & Notes
  • Haomin Luo
    Optic Nerve Research Lab, Devers Eye Institute, Portland, Oregon, United States
    Department of Ophthalmology, The 2nd Xiangya Hospital of Central South University, Changsha, Hunan, China
  • Julian Weichsel
    Ruprecht-Karls-University of Heidelberg, Heidelberg, Germany
  • Stuart Keith Gardiner
    Discoveries in Sight Research Labs, Devers eye Institute, Portland, Oregon, United States
  • Juan Reynaud
    Optic Nerve Research Lab, Devers Eye Institute, Portland, Oregon, United States
  • Christy Hardin
    Optic Nerve Research Lab, Devers Eye Institute, Portland, Oregon, United States
  • Cindy Albert
    Discoveries in Sight Research Labs, Devers eye Institute, Portland, Oregon, United States
  • Jayme R Vianna
    Department of Ophthalmology and Visual Sciences, Dalhousie University, Halifax, Nova Scotia, Canada
  • Glen Sharpe
    Department of Ophthalmology and Visual Sciences, Dalhousie University, Halifax, Nova Scotia, Canada
  • Victoria R Lanoe
    Department of Ophthalmology and Visual Sciences, Dalhousie University, Halifax, Nova Scotia, Canada
  • Jack Quach
    Department of Ophthalmology and Visual Sciences, Dalhousie University, Halifax, Nova Scotia, Canada
  • Shaban Demirel
    Discoveries in Sight Research Labs, Devers eye Institute, Portland, Oregon, United States
  • Brad Fortune
    Discoveries in Sight Research Labs, Devers eye Institute, Portland, Oregon, United States
  • Balwantray C Chauhan
    Department of Ophthalmology and Visual Sciences, Dalhousie University, Halifax, Nova Scotia, Canada
  • Claude F Burgoyne
    Optic Nerve Research Lab, Devers Eye Institute, Portland, Oregon, United States
  • Hongli Yang
    Optic Nerve Research Lab, Devers Eye Institute, Portland, Oregon, United States
  • Footnotes
    Commercial Relationships   Haomin Luo, None; Julian Weichsel, Heidelberg Engineering GmbH (E); Stuart Gardiner, Heidelberg Engineering GmbH (C); Juan Reynaud, None; Christy Hardin, None; Cindy Albert, None; Jayme Vianna, None; Glen Sharpe, None; Victoria Lanoe, None; Jack Quach, None; Shaban Demirel, Carl Zeiss Meditec (C), Heidelberg Engineering GmbH (C), Legacy Good Samaritan Foundation (F); Brad Fortune, Inotek Pharmaceuticals (C), Legacy Good Samaritan Foundation (F); Balwantray Chauhan, Heidelberg Engineering GmbH (F), Heidelberg Engineering GmbH (S), Heidelberg Engineering GmbH (C); Claude Burgoyne, Heidelberg Engineering GmbH (F), Heidelberg Engineering GmbH (C), Heidelberg Engineering GmbH (S); Hongli Yang, None
  • Footnotes
    Support  NIH/NEI R01-EY021281; Good Samaritan Devers Eye Institute Foundation; Unrestrictrcted research support Heidelberg Engineering; Glaucoma Research Society of Canada
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 2097. doi:https://doi.org/
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      Haomin Luo, Julian Weichsel, Stuart Keith Gardiner, Juan Reynaud, Christy Hardin, Cindy Albert, Jayme R Vianna, Glen Sharpe, Victoria R Lanoe, Jack Quach, Shaban Demirel, Brad Fortune, Balwantray C Chauhan, Claude F Burgoyne, Hongli Yang; Automated Segmentation of the Anterior Lamina Cribrosa Surface (ALCS) within Optic Nerve Head (ONH) Optical Coherence Tomography (OCT) B-scans. Invest. Ophthalmol. Vis. Sci. 2018;59(9):2097. doi: https://doi.org/.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To characterize the difference between automated and manual segmentation of the ALCS within ONH OCT B-scans.

Methods : One eye from 397 normal and 342 glaucoma subjects and suspects underwent OCT enhanced depth imaging (Heidelberg Engineering Spectralis) using a 24 radial B-scan pattern. Manual ALCS segmentations were performed using custom software. Approximately 50% of the eyes (198 normal/196 glaucoma) were used to train an automated segmentation algorithm (Spectralis SP-X software 6.4.8.116). The remaining 50% were used to test its performance. Within each B-scan, ALCS z-axis vertical depth (ALCS-Z) relative to the top of image was computed for each pixel at which ALCS could be identified, averaged for each segmentation method, and the absolute difference between the methods (Δ ALCS-Z) was computed. In 16 eyes, ALCS was independently segmented by 4 operators. Bland-Altman limits of agreement among operators (LOA-O) and between the average of the 4 operators (operator average) and automated segmentation (LOA-OA) for ALCS-Z were calculated. The relationship between glaucoma status and Δ ALCS-Z was explored using a General Estimation Equation model.

Results : The final testing dataset included 8653 B-scans with visible ALCS from 361 subjects (191 normal, 170 glaucoma, median age 65, range 20-95). The median (interquartile range) Δ ALCS-Z value was 9.8 (4.6, 16.7) µm in glaucoma and 11.6 (5.6, 21.2) µm in normal eyes. Δ ALCS-Z was greater in normal compared to glaucoma eyes (p <0.0001), however the median differences in both groups were <3 image pixels. Within the 16 reproducibility eyes, the mean ALCS-Z difference between the automated and 4 operator average segmentation values was -1 µm. While the ALCS-Z LOA-OA were -27 to +26 µm, the automated segmentation value was within the LOA-O (-20, +20) in 96.6% of the B-scans (Fig 1). Within the 361 study eyes, 89.4% of automated segmentations fell within the LOA-O (-28 to +28, after adjusting to account for single operator delineation, Fig 2).

Conclusions : Automated ALCS segmentation within 361 normal, glaucoma suspect and subject eyes yielded ALCS z-axis depth values that fell within the between-operator limits of agreement (LOA-O) in 89.4% of B-scans. These findings support further development of automated ALCS segmentation for the detection of glaucoma and its progression.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

 

 

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