An Automatic, Intercapillary Area-based Algorithm for Quantifying Diabetes-related Capillary Dropout Using Optical Coherence Tomography Angiography

Beitrag in einer Fachzeitschrift
(Originalarbeit)


Details zur Publikation

Autor(en): Schottenhamml J, Moult EM, Ploner S, Lee B, Novais EA, Cole ED, Dang S, Lu CD, Husvogt L, Waheed NK, Duker JS, Hornegger J, Fujimoto JG
Zeitschrift: Retina (Philadelphia, Pa.)
Verlag: Lippincott Williams and Wilkins
Jahr der Veröffentlichung: 2016
Band: 36
Heftnummer: 0
Seitenbereich: S93-S101
ISSN: 1539-2864
Sprache: Englisch


Abstract


Purpose: 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.


FAU-Autoren / FAU-Herausgeber

Hornegger, Joachim Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)
Husvogt, Lennart
Lehrstuhl für Informatik 5 (Mustererkennung)
Ploner, Stefan
Lehrstuhl für Informatik 5 (Mustererkennung)
Schottenhamml, Julia
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)


Autor(en) der externen Einrichtung(en)
Massachusetts Institute of Technology (MIT)
Tufts Medical Center


Zitierweisen

APA:
Schottenhamml, J., Moult, E.M., Ploner, S., Lee, B., Novais, E.A., Cole, E.D.,... Fujimoto, J.G. (2016). An Automatic, Intercapillary Area-based Algorithm for Quantifying Diabetes-related Capillary Dropout Using Optical Coherence Tomography Angiography. Retina (Philadelphia, Pa.), 36(0), S93-S101. https://dx.doi.org/10.1097/IAE.0000000000001288

MLA:
Schottenhamml, Julia, et al. "An Automatic, Intercapillary Area-based Algorithm for Quantifying Diabetes-related Capillary Dropout Using Optical Coherence Tomography Angiography." Retina (Philadelphia, Pa.) 36.0 (2016): S93-S101.

BibTeX: 

Zuletzt aktualisiert 2019-09-04 um 00:53