Research Papers: General

Segmentation of the foveal microvasculature using deep learning networks

[+] Author Affiliations
Pavle Prentašić, Sven Lončarić

University of Zagreb, Faculty of Electrical Engineering and Computing, Unska ul. 3, Zagreb 10000, Croatia

Morgan Heisler, Sieun Lee, Mirza Faisal Beg, Marinko Šarunić

Simon Fraser University, Department of Engineering Science, 8888 University Drive, Burnaby, British Columbia V5A1S6, Canada

Zaid Mammo, Andrew Merkur, Eduardo Navajas

University of British Columbia, Department of Ophthalmology and Visual Science, Eye Care Center, 2550 Willow Street, Vancouver, British Columbia V5Z 3N9, Canada

J. Biomed. Opt. 21(7), 075008 (Jul 11, 2016). doi:10.1117/1.JBO.21.7.075008
History: Received February 26, 2016; Accepted June 16, 2016
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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.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Pavle Prentašić ; Morgan Heisler ; Zaid Mammo ; Sieun Lee ; Andrew Merkur, et al.
"Segmentation of the foveal microvasculature using deep learning networks", J. Biomed. Opt. 21(7), 075008 (Jul 11, 2016). ; http://dx.doi.org/10.1117/1.JBO.21.7.075008


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PubMed Articles
STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation. IEEE Trans Pattern Anal Mach Intell Published online Dec 06, 2016;
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