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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Engineering and Computing, Unska ul. 3, Zagreb 10000, Croatia.</nlm:affiliation><country xml:lang="fr">Croatie</country><wicri:regionArea>University of Zagreb, Faculty of Electrical Engineering and Computing, Unska ul. 3, Zagreb 10000</wicri:regionArea><wicri:noRegion>Zagreb 10000</wicri:noRegion></affiliation></author><author><name sortKey="Heisler, Morgan" sort="Heisler, Morgan" uniqKey="Heisler M" first="Morgan" last="Heisler">Morgan Heisler</name><affiliation wicri:level="1"><nlm:affiliation>Simon Fraser University, Department of Engineering Science, 8888 University Drive, Burnaby, British Columbia V5A1S6, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Simon Fraser University, Department of Engineering Science, 8888 University Drive, Burnaby, British Columbia V5A1S6</wicri:regionArea><wicri:noRegion>British Columbia V5A1S6</wicri:noRegion></affiliation></author><author><name sortKey="Mammo, Zaid" sort="Mammo, Zaid" uniqKey="Mammo Z" first="Zaid" 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Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Simon Fraser University, Department of Engineering Science, 8888 University Drive, Burnaby, British Columbia V5A1S6</wicri:regionArea><wicri:noRegion>British Columbia V5A1S6</wicri:noRegion></affiliation></author><author><name sortKey="Merkur, Andrew" sort="Merkur, Andrew" uniqKey="Merkur A" first="Andrew" last="Merkur">Andrew Merkur</name><affiliation wicri:level="4"><nlm:affiliation>University of British Columbia, Department of Ophthalmology and Visual Science, Eye Care Center, 2550 Willow Street, Vancouver, British Columbia V5Z 3N9, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>University of British Columbia, Department of Ophthalmology and Visual Science, Eye Care Center, 2550 Willow Street, Vancouver, British Columbia V5Z 3N9</wicri:regionArea><orgName type="university">Université de la Colombie-Britannique</orgName><placeName><settlement 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uniqKey="Beg M" first="Mirza Faisal" last="Beg">Mirza Faisal Beg</name><affiliation wicri:level="1"><nlm:affiliation>Simon Fraser University, Department of Engineering Science, 8888 University Drive, Burnaby, British Columbia V5A1S6, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Simon Fraser University, Department of Engineering Science, 8888 University Drive, Burnaby, British Columbia V5A1S6</wicri:regionArea><wicri:noRegion>British Columbia V5A1S6</wicri:noRegion></affiliation></author><author><name sortKey="Sarunic, Marinko" sort="Sarunic, Marinko" uniqKey="Sarunic M" first="Marinko" last="Šarunic">Marinko Šarunic</name><affiliation wicri:level="1"><nlm:affiliation>Simon Fraser University, Department of Engineering Science, 8888 University Drive, Burnaby, British Columbia V5A1S6, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Simon Fraser University, Department of Engineering Science, 8888 University Drive, Burnaby, British Columbia V5A1S6</wicri:regionArea><wicri:noRegion>British Columbia V5A1S6</wicri:noRegion></affiliation></author><author><name sortKey="Loncaric, Sven" sort="Loncaric, Sven" uniqKey="Loncaric S" first="Sven" last="Loncaric">Sven Loncaric</name><affiliation wicri:level="1"><nlm:affiliation>University of Zagreb, Faculty of Electrical Engineering and Computing, Unska ul. 3, Zagreb 10000, Croatia.</nlm:affiliation><country xml:lang="fr">Croatie</country><wicri:regionArea>University of Zagreb, Faculty of Electrical Engineering and Computing, Unska ul. 3, Zagreb 10000</wicri:regionArea><wicri:noRegion>Zagreb 10000</wicri:noRegion></affiliation></author></analytic><series><title level="j">Journal of biomedical optics</title><idno type="eISSN">1560-2281</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms (MeSH)</term><term>Fovea Centralis (diagnostic imaging)</term><term>Humans (MeSH)</term><term>Machine Learning (MeSH)</term><term>Microvessels (diagnostic imaging)</term><term>Tomography, Optical Coherence (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes (MeSH)</term><term>Apprentissage machine (MeSH)</term><term>Fossette centrale (imagerie diagnostique)</term><term>Humains (MeSH)</term><term>Microvaisseaux (imagerie diagnostique)</term><term>Tomographie par cohérence optique (MeSH)</term></keywords><keywords scheme="MESH" qualifier="diagnostic imaging" xml:lang="en"><term>Fovea Centralis</term><term>Microvessels</term></keywords><keywords scheme="MESH" qualifier="imagerie diagnostique" xml:lang="fr"><term>Fossette centrale</term><term>Microvaisseaux</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Humans</term><term>Machine Learning</term><term>Tomography, Optical Coherence</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Apprentissage machine</term><term>Humains</term><term>Tomographie par cohérence optique</term></keywords></textClass></profileDesc></teiHeader><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></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">27401936</PMID><DateCompleted><Year>2018</Year><Month>11</Month><Day>15</Day></DateCompleted><DateRevised><Year>2019</Year><Month>09</Month><Day>30</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1560-2281</ISSN><JournalIssue CitedMedium="Internet"><Volume>21</Volume><Issue>7</Issue><PubDate><Year>2016</Year><Month>07</Month><Day>01</Day></PubDate></JournalIssue><Title>Journal of biomedical optics</Title><ISOAbbreviation>J Biomed Opt</ISOAbbreviation></Journal><ArticleTitle>Segmentation of the foveal microvasculature using deep learning networks.</ArticleTitle><Pagination><MedlinePgn>75008</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1117/1.JBO.21.7.075008</ELocationID><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></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Prentašic</LastName><ForeName>Pavle</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>University of Zagreb, Faculty of Electrical Engineering and Computing, Unska ul. 3, Zagreb 10000, Croatia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Heisler</LastName><ForeName>Morgan</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Simon Fraser University, Department of Engineering Science, 8888 University Drive, Burnaby, British Columbia V5A1S6, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mammo</LastName><ForeName>Zaid</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>University of British Columbia, Department of Ophthalmology and Visual Science, Eye Care Center, 2550 Willow Street, Vancouver, British Columbia V5Z 3N9, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lee</LastName><ForeName>Sieun</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Simon Fraser University, Department of Engineering Science, 8888 University Drive, Burnaby, British Columbia V5A1S6, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Merkur</LastName><ForeName>Andrew</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>University of British Columbia, Department of Ophthalmology and Visual Science, Eye Care Center, 2550 Willow Street, Vancouver, British Columbia V5Z 3N9, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Navajas</LastName><ForeName>Eduardo</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>University of British Columbia, Department of Ophthalmology and Visual Science, Eye Care Center, 2550 Willow Street, Vancouver, British Columbia V5Z 3N9, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Beg</LastName><ForeName>Mirza Faisal</ForeName><Initials>MF</Initials><AffiliationInfo><Affiliation>Simon Fraser University, Department of Engineering Science, 8888 University Drive, Burnaby, British Columbia V5A1S6, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Šarunic</LastName><ForeName>Marinko</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Simon Fraser University, Department of Engineering Science, 8888 University Drive, Burnaby, British Columbia V5A1S6, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Loncaric</LastName><ForeName>Sven</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>University of Zagreb, Faculty of Electrical Engineering and Computing, Unska ul. 3, Zagreb 10000, Croatia.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>J Biomed Opt</MedlineTA><NlmUniqueID>9605853</NlmUniqueID><ISSNLinking>1083-3668</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005584" MajorTopicYN="N">Fovea Centralis</DescriptorName><QualifierName UI="Q000000981" MajorTopicYN="Y">diagnostic imaging</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000069550" MajorTopicYN="N">Machine Learning</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D055806" MajorTopicYN="N">Microvessels</DescriptorName><QualifierName UI="Q000000981" MajorTopicYN="Y">diagnostic imaging</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D041623" MajorTopicYN="Y">Tomography, Optical Coherence</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2016</Year><Month>02</Month><Day>26</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2016</Year><Month>06</Month><Day>16</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2016</Year><Month>7</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2016</Year><Month>7</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2018</Year><Month>11</Month><Day>16</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">27401936</ArticleId><ArticleId IdType="pii">2534031</ArticleId><ArticleId IdType="doi">10.1117/1.JBO.21.7.075008</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Canada</li><li>Croatie</li></country><region><li>Colombie-Britannique </li></region><settlement><li>Vancouver</li></settlement><orgName><li>Université de la Colombie-Britannique</li></orgName></list><tree><country name="Croatie"><noRegion><name sortKey="Prentasic, Pavle" sort="Prentasic, Pavle" uniqKey="Prentasic P" first="Pavle" last="Prentašic">Pavle Prentašic</name></noRegion><name sortKey="Loncaric, Sven" sort="Loncaric, Sven" uniqKey="Loncaric S" first="Sven" last="Loncaric">Sven Loncaric</name></country><country name="Canada"><noRegion><name sortKey="Heisler, Morgan" sort="Heisler, Morgan" uniqKey="Heisler M" first="Morgan" last="Heisler">Morgan Heisler</name></noRegion><name sortKey="Beg, Mirza Faisal" sort="Beg, Mirza Faisal" uniqKey="Beg M" first="Mirza Faisal" last="Beg">Mirza Faisal Beg</name><name sortKey="Lee, Sieun" sort="Lee, Sieun" uniqKey="Lee S" first="Sieun" last="Lee">Sieun Lee</name><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></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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