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dc.contributor.authorSchottenhamml, Julia Jennifer
dc.contributor.authorMoult, Eric Michael
dc.contributor.authorPloner, Stefan B
dc.contributor.authorLee, ByungKun
dc.contributor.authorLu, Chen David
dc.contributor.authorFujimoto, James G
dc.date.accessioned2021-02-04T16:22:41Z
dc.date.available2021-02-04T16:22:41Z
dc.date.issued2016-12
dc.identifier.issn0275-004X
dc.identifier.urihttps://hdl.handle.net/1721.1/129675
dc.description.abstractPurpose: To develop a robust, sensitive, and fully automatic algorithm to quantify diabetes-related capillary dropout using optical coherence tomography (OCT) angiography (OCTA). Methods: A 1,050-nm wavelength, 400 kHz A-scan rate swept-source optical coherence tomography prototype was used to perform volumetric optical coherence tomography angiography imaging over 3 mm × 3 mm fields in normal controls (n = 5), patients with diabetes without diabetic retinopathy (DR) (n = 7), patients with nonproliferative diabetic retinopathy (NPDR) (n = 9), and patients with proliferative diabetic retinopathy (PDR) (n = 5); for each patient, one eye was imaged. A fully automatic algorithm to quantify intercapillary areas was developed. Results: Of the 26 evaluated eyes, the segmentation was successful in 22 eyes (85%). The mean values of the 10 and 20 largest intercapillary areas, either including or excluding the foveal avascular zone, showed a consistent trend of increasing size from normal control eyes, to eyes with diabetic retinopathy but without diabetic retinopathy, to nonproliferative diabetic retinopathy eyes, and finally to PDR eyes. Conclusion: Optical coherence tomography angiography-based screening and monitoring of patients with diabetic retinopathy is critically dependent on automated vessel analysis. The algorithm presented was able to automatically extract an intercapillary areabased metric in patients having various stages of diabetic retinopathy. Intercapillary areabased approaches are likely more sensitive to early stage capillary dropout than vascular density-based methods.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (Grants NIH R01-EY011289-29A, R44-EY022864, R01-CA075289-16, FA9550-15-1-0473 and FA9550-12-1-0499)en_US
dc.language.isoen
dc.publisherOvid Technologies (Wolters Kluwer Health)en_US
dc.relation.isversionof10.1097/IAE.0000000000001288en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcePMCen_US
dc.titleAn automatic, intercapillary area based algorithm for quantifying diabetes related capillary dropout using OCT angiographyen_US
dc.typeArticleen_US
dc.identifier.citationSchottenhamml, Julia et al. "An Automatic, Intercapillary Area-Based Algorithm for Quantifying Diabetes-Related Capillary Dropout Using Optical Coherence Tomography Angiography." Retina 36 (December 2016): S93-S101 © 2017 The Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Research Laboratory of Electronicsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Medical Engineering & Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.relation.journalRetinaen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2020-12-11T19:46:54Z
dspace.orderedauthorsSchottenhamml, J; Moult, EM; Ploner, S; Lee, B; Novais, EA; Cole, E; Dang, S; Lu, CD; Husvogt, L; Waheed, NK; Duker, JS; Hornegger, J; Fujimoto, JGen_US
dspace.date.submission2020-12-11T19:47:01Z
mit.journal.volume36en_US
mit.journal.issue12en_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusComplete


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