title: Segmentation of the foveal microvasculature using deep learning networks.
abstract: Accurate segmentation of the retinal microvasculature is a critical step in the quantitative analysis of the retinal circulation, which can be an important marker in evaluating the severity of retinal diseases. As manual segmentation remains the gold standard for segmentation of optical coherence tomography angiography (OCT-A) images, we present a method for automating the segmentation of OCT-A images using deep neural networks (DNNs). Eighty OCT-A images of the foveal region in 12 eyes from 6 healthy volunteers were acquired using a prototype OCT-A system and subsequently manually segmented. The automated segmentation of the blood vessels in the OCT-A images was then performed by classifying each pixel into vessel or nonvessel class using deep convolutional neural networks. When the automated results were compared against the manual segmentation results, a maximum mean accuracy of 0.83 was obtained. When the automated results were compared with inter and intrarater accuracies, the automated results were shown to be comparable to the human raters suggesting that segmentation using DNNs is comparable to a second manual rater. As manually segmenting the retinal microvasculature is a tedious task, having a reliable automated output such as automated segmentation by DNNs, is an important step in creating an automated output.
authors.affiliation: University of Zagreb, Faculty of Electrical Engineering and Computing, Unska ul. 3, Zagreb 10000, Croatia.
authors.forename: Pavle
authors.initials: P
authors.lastname: Prentašic
authors.affiliation: Simon Fraser University, Department of Engineering Science, 8888 University Drive, Burnaby, British Columbia V5A1S6, Canada.
authors.forename: Morgan
authors.initials: M
authors.lastname: Heisler
authors.affiliation: University of British Columbia, Department of Ophthalmology and Visual Science, Eye Care Center, 2550 Willow Street, Vancouver, British Columbia V5Z 3N9, Canada.
authors.forename: Zaid
authors.initials: Z
authors.lastname: Mammo
authors.affiliation: Simon Fraser University, Department of Engineering Science, 8888 University Drive, Burnaby, British Columbia V5A1S6, Canada.
authors.forename: Sieun
authors.initials: S
authors.lastname: Lee
authors.affiliation: University of British Columbia, Department of Ophthalmology and Visual Science, Eye Care Center, 2550 Willow Street, Vancouver, British Columbia V5Z 3N9, Canada.
authors.forename: Andrew
authors.initials: A
authors.lastname: Merkur
authors.affiliation: University of British Columbia, Department of Ophthalmology and Visual Science, Eye Care Center, 2550 Willow Street, Vancouver, British Columbia V5Z 3N9, Canada.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Navajas
authors.affiliation: Simon Fraser University, Department of Engineering Science, 8888 University Drive, Burnaby, British Columbia V5A1S6, Canada.
authors.forename: Mirza Faisal
authors.initials: MF
authors.lastname: Beg
authors.affiliation: Simon Fraser University, Department of Engineering Science, 8888 University Drive, Burnaby, British Columbia V5A1S6, Canada.
authors.forename: Marinko
authors.initials: M
authors.lastname: Šarunic
authors.affiliation: University of Zagreb, Faculty of Electrical Engineering and Computing, Unska ul. 3, Zagreb 10000, Croatia.
authors.forename: Sven
authors.initials: S
authors.lastname: Loncaric
mesh_terms: D000465:Algorithms; D005584:Fovea Centralis / Q000000981:diagnostic imaging*; D006801:Humans; D000069550:Machine Learning; D055806:Microvessels / Q000000981:diagnostic imaging*; D041623:Tomography, Optical Coherence