Automated quantification of superficial retinal capillaries and large vessels for diabetic retinopathy on optical coherence tomographic angiography
Optical coherence tomography angiography (OCTA) is a relatively new technique with capillary‐level resolution, which has shown great potential for the diagnosis of diabetic retinopathy (DR). A fully automatic algorithm for the quantitative measurement of microcirculatory changes in sight‐threatening...
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description | Optical coherence tomography angiography (OCTA) is a relatively new technique with capillary‐level resolution, which has shown great potential for the diagnosis of diabetic retinopathy (DR). A fully automatic algorithm for the quantitative measurement of microcirculatory changes in sight‐threatening DR is presented. The foveal avascular zone (FAZ) segmentation was improved with a graph‐theoretic method and the large vessels and capillaries were separately identified and analyzed. The method was evaluated in healthy and diabetic eyes with various stages of retinopathy. Results showed that, compared with the healthy group, the diabetic group showed a significantly larger large vessel density, but a significantly smaller capillary density (P |
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A fully automatic algorithm for the quantitative measurement of microcirculatory changes in sight‐threatening diabetic retinopathy (DR) is presented in this paper. The foveal avascular zone segmentation was improved with a graph‐theoretic method and various image metrics were extracted. The combined variable of all image metrics reached an area under the ROC of 0.853 for mild to moderate nonproliferative DR and 0.950 for proliferative DR.</description><identifier>ISSN: 1864-063X</identifier><identifier>EISSN: 1864-0648</identifier><identifier>DOI: 10.1002/jbio.201900103</identifier><identifier>PMID: 31309729</identifier><language>eng</language><publisher>Weinheim: WILEY‐VCH Verlag GmbH & Co. KGaA</publisher><subject>Algorithms ; Angiography ; Automation ; Biomarkers ; Blood vessels ; Capillaries ; Density ; Diabetes ; Diabetes mellitus ; Diabetic retinopathy ; Eye (anatomy) ; Image processing ; Image segmentation ; Medical imaging ; microcirculation ; Microvasculature ; OCT angiography ; Optical Coherence Tomography ; Retina ; Retinopathy</subject><ispartof>Journal of biophotonics, 2019-11, Vol.12 (11), p.e201900103-n/a</ispartof><rights>2019 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim</rights><rights>2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3733-f1e7acb0b76c77bb9ad510b619ad6c26c77fb18e31f5e8decf358c522d211b313</citedby><cites>FETCH-LOGICAL-c3733-f1e7acb0b76c77bb9ad510b619ad6c26c77fb18e31f5e8decf358c522d211b313</cites><orcidid>0000-0002-5286-0454</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjbio.201900103$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjbio.201900103$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1416,27923,27924,45573,45574</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31309729$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Xu, Xiayu</creatorcontrib><creatorcontrib>Chen, Cheng</creatorcontrib><creatorcontrib>Ding, Wenxiang</creatorcontrib><creatorcontrib>Yang, Peiwei</creatorcontrib><creatorcontrib>Lu, Huiqin</creatorcontrib><creatorcontrib>Xu, Feng</creatorcontrib><creatorcontrib>Lei, Jianqin</creatorcontrib><title>Automated quantification of superficial retinal capillaries and large vessels for diabetic retinopathy on optical coherence tomographic angiography</title><title>Journal of biophotonics</title><addtitle>J Biophotonics</addtitle><description>Optical coherence tomography angiography (OCTA) is a relatively new technique with capillary‐level resolution, which has shown great potential for the diagnosis of diabetic retinopathy (DR). A fully automatic algorithm for the quantitative measurement of microcirculatory changes in sight‐threatening DR is presented. The foveal avascular zone (FAZ) segmentation was improved with a graph‐theoretic method and the large vessels and capillaries were separately identified and analyzed. The method was evaluated in healthy and diabetic eyes with various stages of retinopathy. Results showed that, compared with the healthy group, the diabetic group showed a significantly larger large vessel density, but a significantly smaller capillary density (P < .001). Circularity of FAZ was significantly smaller while nonperfusion area was significantly larger in the diabetic group. The combined variable of all image metrics reached an area under the ROC of 0.853 (95% CI, 0.784‐0.923) for mild to moderate nonproliferative DR and 0.950 (95% CI, 0.922‐0.979) for proliferative DR. Microvascular and FAZ changes with various DR stages can be accurately delineated using the developed automatic program. Quantitative metrics on OCTA serve as potential biomarkers for the staging of DR.
A fully automatic algorithm for the quantitative measurement of microcirculatory changes in sight‐threatening diabetic retinopathy (DR) is presented in this paper. The foveal avascular zone segmentation was improved with a graph‐theoretic method and various image metrics were extracted. The combined variable of all image metrics reached an area under the ROC of 0.853 for mild to moderate nonproliferative DR and 0.950 for proliferative DR.</description><subject>Algorithms</subject><subject>Angiography</subject><subject>Automation</subject><subject>Biomarkers</subject><subject>Blood vessels</subject><subject>Capillaries</subject><subject>Density</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetic retinopathy</subject><subject>Eye (anatomy)</subject><subject>Image processing</subject><subject>Image segmentation</subject><subject>Medical imaging</subject><subject>microcirculation</subject><subject>Microvasculature</subject><subject>OCT angiography</subject><subject>Optical Coherence Tomography</subject><subject>Retina</subject><subject>Retinopathy</subject><issn>1864-063X</issn><issn>1864-0648</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNqFkU9rGzEQxUVJqRO31x6DIJde7I4k779jYpLUxeBLC70tknZky6xXG2k3wZ-jXzha1nUgl57mafjNGzSPkK8M5gyAf98r6-YcWAHAQHwglyxPFzNIF_nFWYs_E3IVwh4gBZGIT2QimIAi48Ul-Xvbd-4gO6zoUy-bzhqrZWddQ52hoW_Rx4aVNfXY2SZWLVtb19JbDFQ2FY1yi_QZQ8A6UOM8raxUEdbjiGtltzvSwbCNzcHB7dBjo5HGzW7rZbuLsGy2dnwcP5OPRtYBv5zqlPx-uP-1_DFbbx5Xy9v1TItMiJlhmEmtQGWpzjKlClklDFTKokg1H5pGsRwFMwnmFWojklwnnFecMRVPMCXfRt_Wu6ceQ1cebNAYf9eg60PJeZJnYgFZEdGbd-je9T7eI1KC8TSFJN55SuYjpb0LwaMpW28P0h9LBuUQVznEVZ7jigPXJ9teHbA64__yiUAxAi-2xuN_7Mqfd6vNm_krHBGlrg</recordid><startdate>201911</startdate><enddate>201911</enddate><creator>Xu, Xiayu</creator><creator>Chen, Cheng</creator><creator>Ding, Wenxiang</creator><creator>Yang, Peiwei</creator><creator>Lu, Huiqin</creator><creator>Xu, Feng</creator><creator>Lei, Jianqin</creator><general>WILEY‐VCH Verlag GmbH & Co. KGaA</general><general>Wiley Subscription Services, Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7SP</scope><scope>7SR</scope><scope>7U5</scope><scope>8FD</scope><scope>FR3</scope><scope>JG9</scope><scope>K9.</scope><scope>L7M</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-5286-0454</orcidid></search><sort><creationdate>201911</creationdate><title>Automated quantification of superficial retinal capillaries and large vessels for diabetic retinopathy on optical coherence tomographic angiography</title><author>Xu, Xiayu ; Chen, Cheng ; Ding, Wenxiang ; Yang, Peiwei ; Lu, Huiqin ; Xu, Feng ; Lei, Jianqin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3733-f1e7acb0b76c77bb9ad510b619ad6c26c77fb18e31f5e8decf358c522d211b313</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Angiography</topic><topic>Automation</topic><topic>Biomarkers</topic><topic>Blood vessels</topic><topic>Capillaries</topic><topic>Density</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetic retinopathy</topic><topic>Eye (anatomy)</topic><topic>Image processing</topic><topic>Image segmentation</topic><topic>Medical imaging</topic><topic>microcirculation</topic><topic>Microvasculature</topic><topic>OCT angiography</topic><topic>Optical Coherence Tomography</topic><topic>Retina</topic><topic>Retinopathy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xu, Xiayu</creatorcontrib><creatorcontrib>Chen, Cheng</creatorcontrib><creatorcontrib>Ding, Wenxiang</creatorcontrib><creatorcontrib>Yang, Peiwei</creatorcontrib><creatorcontrib>Lu, Huiqin</creatorcontrib><creatorcontrib>Xu, Feng</creatorcontrib><creatorcontrib>Lei, Jianqin</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of biophotonics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xu, Xiayu</au><au>Chen, Cheng</au><au>Ding, Wenxiang</au><au>Yang, Peiwei</au><au>Lu, Huiqin</au><au>Xu, Feng</au><au>Lei, Jianqin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automated quantification of superficial retinal capillaries and large vessels for diabetic retinopathy on optical coherence tomographic angiography</atitle><jtitle>Journal of biophotonics</jtitle><addtitle>J Biophotonics</addtitle><date>2019-11</date><risdate>2019</risdate><volume>12</volume><issue>11</issue><spage>e201900103</spage><epage>n/a</epage><pages>e201900103-n/a</pages><issn>1864-063X</issn><eissn>1864-0648</eissn><abstract>Optical coherence tomography angiography (OCTA) is a relatively new technique with capillary‐level resolution, which has shown great potential for the diagnosis of diabetic retinopathy (DR). A fully automatic algorithm for the quantitative measurement of microcirculatory changes in sight‐threatening DR is presented. The foveal avascular zone (FAZ) segmentation was improved with a graph‐theoretic method and the large vessels and capillaries were separately identified and analyzed. The method was evaluated in healthy and diabetic eyes with various stages of retinopathy. Results showed that, compared with the healthy group, the diabetic group showed a significantly larger large vessel density, but a significantly smaller capillary density (P < .001). Circularity of FAZ was significantly smaller while nonperfusion area was significantly larger in the diabetic group. The combined variable of all image metrics reached an area under the ROC of 0.853 (95% CI, 0.784‐0.923) for mild to moderate nonproliferative DR and 0.950 (95% CI, 0.922‐0.979) for proliferative DR. Microvascular and FAZ changes with various DR stages can be accurately delineated using the developed automatic program. Quantitative metrics on OCTA serve as potential biomarkers for the staging of DR.
A fully automatic algorithm for the quantitative measurement of microcirculatory changes in sight‐threatening diabetic retinopathy (DR) is presented in this paper. The foveal avascular zone segmentation was improved with a graph‐theoretic method and various image metrics were extracted. The combined variable of all image metrics reached an area under the ROC of 0.853 for mild to moderate nonproliferative DR and 0.950 for proliferative DR.</abstract><cop>Weinheim</cop><pub>WILEY‐VCH Verlag GmbH & Co. KGaA</pub><pmid>31309729</pmid><doi>10.1002/jbio.201900103</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-5286-0454</orcidid></addata></record> |
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subjects | Algorithms Angiography Automation Biomarkers Blood vessels Capillaries Density Diabetes Diabetes mellitus Diabetic retinopathy Eye (anatomy) Image processing Image segmentation Medical imaging microcirculation Microvasculature OCT angiography Optical Coherence Tomography Retina Retinopathy |
title | Automated quantification of superficial retinal capillaries and large vessels for diabetic retinopathy on optical coherence tomographic angiography |
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