Segmentation of the foveal microvasculature using deep learning networks.
Identifieur interne : 000B58 ( Main/Exploration ); précédent : 000B57; suivant : 000B59Segmentation of the foveal microvasculature using deep learning networks.
Auteurs : Pavle Prentašic [Croatie] ; Morgan Heisler [Canada] ; Zaid Mammo [Canada] ; Sieun Lee [Canada] ; Andrew Merkur [Canada] ; Eduardo Navajas [Canada] ; Mirza Faisal Beg [Canada] ; Marinko Šarunic [Canada] ; Sven Loncaric [Croatie]Source :
- Journal of biomedical optics [ 1560-2281 ] ; 2016.
Descripteurs français
- KwdFr :
- MESH :
- imagerie diagnostique : Fossette centrale, Microvaisseaux.
- Algorithmes, Apprentissage machine, Humains, Tomographie par cohérence optique.
English descriptors
- KwdEn :
- MESH :
- diagnostic imaging : Fovea Centralis, Microvessels.
- Algorithms, Humans, Machine Learning, Tomography, Optical Coherence.
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.
DOI: 10.1117/1.JBO.21.7.075008
PubMed: 27401936
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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<term>Fovea Centralis (diagnostic imaging)</term>
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<term>Machine Learning (MeSH)</term>
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<front><div type="abstract" xml:lang="en">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.</div>
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<Abstract><AbstractText>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.</AbstractText>
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<name sortKey="Loncaric, Sven" sort="Loncaric, Sven" uniqKey="Loncaric S" first="Sven" last="Loncaric">Sven Loncaric</name>
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<country name="Canada"><noRegion><name sortKey="Heisler, Morgan" sort="Heisler, Morgan" uniqKey="Heisler M" first="Morgan" last="Heisler">Morgan Heisler</name>
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<name sortKey="Beg, Mirza Faisal" sort="Beg, Mirza Faisal" uniqKey="Beg M" first="Mirza Faisal" last="Beg">Mirza Faisal Beg</name>
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<name sortKey="Mammo, Zaid" sort="Mammo, Zaid" uniqKey="Mammo Z" first="Zaid" last="Mammo">Zaid Mammo</name>
<name sortKey="Merkur, Andrew" sort="Merkur, Andrew" uniqKey="Merkur A" first="Andrew" last="Merkur">Andrew Merkur</name>
<name sortKey="Navajas, Eduardo" sort="Navajas, Eduardo" uniqKey="Navajas E" first="Eduardo" last="Navajas">Eduardo Navajas</name>
<name sortKey="Sarunic, Marinko" sort="Sarunic, Marinko" uniqKey="Sarunic M" first="Marinko" last="Šarunic">Marinko Šarunic</name>
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