Automated quantification of nonperfusion in three retinal plexuses using projection-resolved optical coherence tomography angiography in diabetic retinopathy

Miao Zhang, Thomas Hwang, Changlei Dongye, David Wilson, David Huang, Jia Yali

Research output: Contribution to journalArticle

45 Citations (Scopus)

Abstract

Purpose. The Purpose of this study was to evaluate an automated algorithm for detecting avascular area (AA) in optical coherence tomography angiograms (OCTAs) separated into three individual plexuses using a projection-resolved technique. Methods. A3☓ 3 mm macular OCTA was obtained in 13 healthy and 13 mild nonproliferative diabetic retinopathy (NPDR) participants. A projection-resolved algorithm segmented OCTA into three vascular plexuses: superficial, intermediate, and deep. An automated algorithm detected AA in each of the three plexuses that were segmented and in the combined inner-retinal angiograms. We assessed the diagnostic accuracy of extrafoveal and total AA using segmented and combined angiograms, the agreement between automated and manual detection of AA, and the within-visit repeatability. Results. The sum of extrafoveal AA from the segmented angiograms was larger in the NPDR group by 0.17 mm2 (P < 0.001) and detected NPDR with 94.6% sensitivity (area under the receiver operating characteristic curve [AROC] = 0.99). In the combined inner-retinal angiograms, the extrafoveal AA was larger in the NPDR group by 0.01 mm2 (P = 0.168) and detected NPDR with 26.9% sensitivity (AROC = 0.62). The total AA, inclusive of the foveal avascular zone, in the segmented and combined angiograms, detected NPDR with 23.1% and 7.7% sensitivity, respectively. The agreement between the manual and automated detection of AA had a Jaccard index of >0.8. The pooled SDs of AA were small compared with the difference in mean for control and NPDR groups. Conclusions. An algorithm to detect AA in OCTA separated into three individual plexuses using a projection-resolved algorithm accurately distinguishes mild NPDR from control eyes. Automatically detected AA agrees with manual delineation and is highly repeatable.

Original languageEnglish (US)
Pages (from-to)5101-5106
Number of pages6
JournalInvestigative Ophthalmology and Visual Science
Volume57
Issue number13
DOIs
StatePublished - Oct 1 2016

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Optical Coherence Tomography
Diabetic Retinopathy
Angiography
Blood Vessels

Keywords

  • Avascular area
  • Diabetic retinopathy
  • Optical coherence tomography angiography

ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems
  • Cellular and Molecular Neuroscience

Cite this

@article{82dfe3dc6d8141858a6bd9ed477a61fa,
title = "Automated quantification of nonperfusion in three retinal plexuses using projection-resolved optical coherence tomography angiography in diabetic retinopathy",
abstract = "Purpose. The Purpose of this study was to evaluate an automated algorithm for detecting avascular area (AA) in optical coherence tomography angiograms (OCTAs) separated into three individual plexuses using a projection-resolved technique. Methods. A3☓ 3 mm macular OCTA was obtained in 13 healthy and 13 mild nonproliferative diabetic retinopathy (NPDR) participants. A projection-resolved algorithm segmented OCTA into three vascular plexuses: superficial, intermediate, and deep. An automated algorithm detected AA in each of the three plexuses that were segmented and in the combined inner-retinal angiograms. We assessed the diagnostic accuracy of extrafoveal and total AA using segmented and combined angiograms, the agreement between automated and manual detection of AA, and the within-visit repeatability. Results. The sum of extrafoveal AA from the segmented angiograms was larger in the NPDR group by 0.17 mm2 (P < 0.001) and detected NPDR with 94.6{\%} sensitivity (area under the receiver operating characteristic curve [AROC] = 0.99). In the combined inner-retinal angiograms, the extrafoveal AA was larger in the NPDR group by 0.01 mm2 (P = 0.168) and detected NPDR with 26.9{\%} sensitivity (AROC = 0.62). The total AA, inclusive of the foveal avascular zone, in the segmented and combined angiograms, detected NPDR with 23.1{\%} and 7.7{\%} sensitivity, respectively. The agreement between the manual and automated detection of AA had a Jaccard index of >0.8. The pooled SDs of AA were small compared with the difference in mean for control and NPDR groups. Conclusions. An algorithm to detect AA in OCTA separated into three individual plexuses using a projection-resolved algorithm accurately distinguishes mild NPDR from control eyes. Automatically detected AA agrees with manual delineation and is highly repeatable.",
keywords = "Avascular area, Diabetic retinopathy, Optical coherence tomography angiography",
author = "Miao Zhang and Thomas Hwang and Changlei Dongye and David Wilson and David Huang and Jia Yali",
year = "2016",
month = "10",
day = "1",
doi = "10.1167/iovs.16-19776",
language = "English (US)",
volume = "57",
pages = "5101--5106",
journal = "Investigative Ophthalmology and Visual Science",
issn = "0146-0404",
publisher = "Association for Research in Vision and Ophthalmology Inc.",
number = "13",

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TY - JOUR

T1 - Automated quantification of nonperfusion in three retinal plexuses using projection-resolved optical coherence tomography angiography in diabetic retinopathy

AU - Zhang, Miao

AU - Hwang, Thomas

AU - Dongye, Changlei

AU - Wilson, David

AU - Huang, David

AU - Yali, Jia

PY - 2016/10/1

Y1 - 2016/10/1

N2 - Purpose. The Purpose of this study was to evaluate an automated algorithm for detecting avascular area (AA) in optical coherence tomography angiograms (OCTAs) separated into three individual plexuses using a projection-resolved technique. Methods. A3☓ 3 mm macular OCTA was obtained in 13 healthy and 13 mild nonproliferative diabetic retinopathy (NPDR) participants. A projection-resolved algorithm segmented OCTA into three vascular plexuses: superficial, intermediate, and deep. An automated algorithm detected AA in each of the three plexuses that were segmented and in the combined inner-retinal angiograms. We assessed the diagnostic accuracy of extrafoveal and total AA using segmented and combined angiograms, the agreement between automated and manual detection of AA, and the within-visit repeatability. Results. The sum of extrafoveal AA from the segmented angiograms was larger in the NPDR group by 0.17 mm2 (P < 0.001) and detected NPDR with 94.6% sensitivity (area under the receiver operating characteristic curve [AROC] = 0.99). In the combined inner-retinal angiograms, the extrafoveal AA was larger in the NPDR group by 0.01 mm2 (P = 0.168) and detected NPDR with 26.9% sensitivity (AROC = 0.62). The total AA, inclusive of the foveal avascular zone, in the segmented and combined angiograms, detected NPDR with 23.1% and 7.7% sensitivity, respectively. The agreement between the manual and automated detection of AA had a Jaccard index of >0.8. The pooled SDs of AA were small compared with the difference in mean for control and NPDR groups. Conclusions. An algorithm to detect AA in OCTA separated into three individual plexuses using a projection-resolved algorithm accurately distinguishes mild NPDR from control eyes. Automatically detected AA agrees with manual delineation and is highly repeatable.

AB - Purpose. The Purpose of this study was to evaluate an automated algorithm for detecting avascular area (AA) in optical coherence tomography angiograms (OCTAs) separated into three individual plexuses using a projection-resolved technique. Methods. A3☓ 3 mm macular OCTA was obtained in 13 healthy and 13 mild nonproliferative diabetic retinopathy (NPDR) participants. A projection-resolved algorithm segmented OCTA into three vascular plexuses: superficial, intermediate, and deep. An automated algorithm detected AA in each of the three plexuses that were segmented and in the combined inner-retinal angiograms. We assessed the diagnostic accuracy of extrafoveal and total AA using segmented and combined angiograms, the agreement between automated and manual detection of AA, and the within-visit repeatability. Results. The sum of extrafoveal AA from the segmented angiograms was larger in the NPDR group by 0.17 mm2 (P < 0.001) and detected NPDR with 94.6% sensitivity (area under the receiver operating characteristic curve [AROC] = 0.99). In the combined inner-retinal angiograms, the extrafoveal AA was larger in the NPDR group by 0.01 mm2 (P = 0.168) and detected NPDR with 26.9% sensitivity (AROC = 0.62). The total AA, inclusive of the foveal avascular zone, in the segmented and combined angiograms, detected NPDR with 23.1% and 7.7% sensitivity, respectively. The agreement between the manual and automated detection of AA had a Jaccard index of >0.8. The pooled SDs of AA were small compared with the difference in mean for control and NPDR groups. Conclusions. An algorithm to detect AA in OCTA separated into three individual plexuses using a projection-resolved algorithm accurately distinguishes mild NPDR from control eyes. Automatically detected AA agrees with manual delineation and is highly repeatable.

KW - Avascular area

KW - Diabetic retinopathy

KW - Optical coherence tomography angiography

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