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

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 (2016)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2016

Journal

Retina-The Journal of Retinal and Vitreous Diseases Lippincott, Williams & Wilkins

Publisher: Lippincott Williams and Wilkins

Book Volume: 36

Pages Range: S93-S101

Journal Issue: 0

URI: https://www.ncbi.nlm.nih.gov/pubmed/28005667

DOI: 10.1097/IAE.0000000000001288

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.

Authors with CRIS profile

Julia Schottenhamml Stefan Ploner Lehrstuhl für Informatik 5 (Mustererkennung) Lennart Husvogt Lehrstuhl für Informatik 5 (Mustererkennung) Joachim Hornegger Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) (LME)

Involved external institutions

Massachusetts Institute of Technology (MIT) US United States (USA) (US) Tufts Medical Center US United States (USA) (US)

How to cite

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-The Journal of Retinal and Vitreous Diseases, 36(0), S93-S101. https://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-The Journal of Retinal and Vitreous Diseases 36.0 (2016): S93-S101.

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