11 July 2016 Segmentation of the foveal microvasculature using deep learning networks
Author Affiliations +
J. of Biomedical Optics, 21(7), 075008 (2016). doi:10.1117/1.JBO.21.7.075008
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.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
Pavle Prentašić, Morgan Heisler, Zaid Mammo, Sieun Lee, Andrew Merkur, Eduardo Navajas, Mirza Faisal Beg, Marinko Šarunic, Sven Lončarić, "Segmentation of the foveal microvasculature using deep learning networks," Journal of Biomedical Optics 21(7), 075008 (11 July 2016). https://doi.org/10.1117/1.JBO.21.7.075008

Image segmentation


Blood vessels


Optical coherence tomography

Neural networks


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