3D Shape Modeling and Analysis of Retinal Microvasculature in OCT-Angiography Images
3D optical coherence tomography angiography (OCT-A) is a novel and non-invasive imaging modality for analyzing retinal diseases. The studies of microvasculature in 2D en face projection images have been widely implemented, but comprehensive 3D analysis of OCT-A images with rich depth-resolved microv...
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| Veröffentlicht in: | IEEE transactions on medical imaging 2020-05, Vol.39 (5), p.1335-1346 |
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| creator | Zhang, Jiong Qiao, Yuchuan Sarabi, Mona Sharifi Khansari, Maziyar M. Gahm, Jin K. Kashani, Amir H. Shi, Yonggang |
| description | 3D optical coherence tomography angiography (OCT-A) is a novel and non-invasive imaging modality for analyzing retinal diseases. The studies of microvasculature in 2D en face projection images have been widely implemented, but comprehensive 3D analysis of OCT-A images with rich depth-resolved microvascular information is rarely considered. In this paper, we propose a robust, effective, and automatic 3D shape modeling framework to provide a high-quality 3D vessel representation and to preserve valuable 3D geometric and topological information for vessel analysis. Effective vessel enhancement and extraction steps by means of curvelet denoising and optimally oriented flux (OOF) filtering are first designed to produce 3D microvascular networks. Afterwards, a novel 3D data representation of OCT-A microvasculature is reconstructed via advanced mesh reconstruction techniques. Based on the 3D surfaces, shape analysis is established to extract novel shape-based microvascular area distortion via the Laplace-Beltrami eigen-projection. The extracted feature is integrated into a graph-cut segmentation system to categorize large vessels and small capillaries for more precise shape analysis. The proposed framework is validated on a dedicated repeated scan dataset including 260 volume images and shows high repeatability. Statistical analysis using the surface area biomarker is performed on small capillaries to avoid the effect of tailing artifact from large vessels. It shows significant differences (p |
| doi_str_mv | 10.1109/TMI.2019.2948867 |
| format | Article |
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The studies of microvasculature in 2D en face projection images have been widely implemented, but comprehensive 3D analysis of OCT-A images with rich depth-resolved microvascular information is rarely considered. In this paper, we propose a robust, effective, and automatic 3D shape modeling framework to provide a high-quality 3D vessel representation and to preserve valuable 3D geometric and topological information for vessel analysis. Effective vessel enhancement and extraction steps by means of curvelet denoising and optimally oriented flux (OOF) filtering are first designed to produce 3D microvascular networks. Afterwards, a novel 3D data representation of OCT-A microvasculature is reconstructed via advanced mesh reconstruction techniques. Based on the 3D surfaces, shape analysis is established to extract novel shape-based microvascular area distortion via the Laplace-Beltrami eigen-projection. The extracted feature is integrated into a graph-cut segmentation system to categorize large vessels and small capillaries for more precise shape analysis. The proposed framework is validated on a dedicated repeated scan dataset including 260 volume images and shows high repeatability. Statistical analysis using the surface area biomarker is performed on small capillaries to avoid the effect of tailing artifact from large vessels. It shows significant differences (p<; 0.001) between DR stages on 100 subjects in a OCTA-DR dataset. The proposed shape modeling and analysis framework opens the possibility for further investigating OCT-A microvasculature in a new perspective.</description><identifier>ISSN: 0278-0062</identifier><identifier>EISSN: 1558-254X</identifier><identifier>DOI: 10.1109/TMI.2019.2948867</identifier><identifier>PMID: 31647423</identifier><identifier>CODEN: ITMID4</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Angiography ; Biomarkers ; Blood vessels ; Capillaries ; Datasets ; Diabetic retinopathy ; Feature extraction ; Finite element method ; Image reconstruction ; Image segmentation ; Medical imaging ; Microvasculature ; Modelling ; Noise reduction ; Optical Coherence Tomography ; Optical coherence tomography angiography ; Representations ; Retina ; retinal microvasculature ; Shape ; shape analysis ; Solid modeling ; Statistical analysis ; Surface reconstruction ; Surface treatment ; Three dimensional models ; Three-dimensional displays</subject><ispartof>IEEE transactions on medical imaging, 2020-05, Vol.39 (5), p.1335-1346</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c444t-b7eb1c1df60b3d1f042e7ee12272e7113ead86ac80626ba7939b03fef973e9163</citedby><cites>FETCH-LOGICAL-c444t-b7eb1c1df60b3d1f042e7ee12272e7113ead86ac80626ba7939b03fef973e9163</cites><orcidid>0000-0002-3466-4302 ; 0000-0002-1922-9118</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8879535$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,777,781,793,882,27905,27906,54739</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8879535$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31647423$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, Jiong</creatorcontrib><creatorcontrib>Qiao, Yuchuan</creatorcontrib><creatorcontrib>Sarabi, Mona Sharifi</creatorcontrib><creatorcontrib>Khansari, Maziyar M.</creatorcontrib><creatorcontrib>Gahm, Jin K.</creatorcontrib><creatorcontrib>Kashani, Amir H.</creatorcontrib><creatorcontrib>Shi, Yonggang</creatorcontrib><title>3D Shape Modeling and Analysis of Retinal Microvasculature in OCT-Angiography Images</title><title>IEEE transactions on medical imaging</title><addtitle>TMI</addtitle><addtitle>IEEE Trans Med Imaging</addtitle><description>3D optical coherence tomography angiography (OCT-A) is a novel and non-invasive imaging modality for analyzing retinal diseases. The studies of microvasculature in 2D en face projection images have been widely implemented, but comprehensive 3D analysis of OCT-A images with rich depth-resolved microvascular information is rarely considered. In this paper, we propose a robust, effective, and automatic 3D shape modeling framework to provide a high-quality 3D vessel representation and to preserve valuable 3D geometric and topological information for vessel analysis. Effective vessel enhancement and extraction steps by means of curvelet denoising and optimally oriented flux (OOF) filtering are first designed to produce 3D microvascular networks. Afterwards, a novel 3D data representation of OCT-A microvasculature is reconstructed via advanced mesh reconstruction techniques. Based on the 3D surfaces, shape analysis is established to extract novel shape-based microvascular area distortion via the Laplace-Beltrami eigen-projection. The extracted feature is integrated into a graph-cut segmentation system to categorize large vessels and small capillaries for more precise shape analysis. The proposed framework is validated on a dedicated repeated scan dataset including 260 volume images and shows high repeatability. Statistical analysis using the surface area biomarker is performed on small capillaries to avoid the effect of tailing artifact from large vessels. It shows significant differences (p<; 0.001) between DR stages on 100 subjects in a OCTA-DR dataset. The proposed shape modeling and analysis framework opens the possibility for further investigating OCT-A microvasculature in a new perspective.</description><subject>Angiography</subject><subject>Biomarkers</subject><subject>Blood vessels</subject><subject>Capillaries</subject><subject>Datasets</subject><subject>Diabetic retinopathy</subject><subject>Feature extraction</subject><subject>Finite element method</subject><subject>Image reconstruction</subject><subject>Image segmentation</subject><subject>Medical imaging</subject><subject>Microvasculature</subject><subject>Modelling</subject><subject>Noise reduction</subject><subject>Optical Coherence Tomography</subject><subject>Optical coherence tomography angiography</subject><subject>Representations</subject><subject>Retina</subject><subject>retinal microvasculature</subject><subject>Shape</subject><subject>shape analysis</subject><subject>Solid modeling</subject><subject>Statistical analysis</subject><subject>Surface reconstruction</subject><subject>Surface treatment</subject><subject>Three dimensional models</subject><subject>Three-dimensional displays</subject><issn>0278-0062</issn><issn>1558-254X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkdGLEzEQxoMoXu_0XRAk4IsvWzNJdpO8CKV6WrhyoBV8C9nd2W2O7aa32T3of29Ka1GfZob5zcfMfIS8ATYHYObjZr2acwZmzo3UulDPyAzyXGc8l7-ekxnjSmeMFfyKXMf4wBjInJmX5EpAIZXkYkY24jP9sXV7pOtQY-f7lrq-povedYfoIw0N_Y6jTyVd-2oITy5WU-fGaUDqe3q_3GSLvvWhHdx-e6CrnWsxviIvGtdFfH2ON-Tn7ZfN8lt2d_91tVzcZZWUcsxKhSVUUDcFK0UNDZMcFSJwrlICINDVunCVThcUpVNGmJKJBhujBBooxA35dNLdT-UO6wr7cXCd3Q9-54aDDc7bfzu939o2PFkFSoJQSeDDWWAIjxPG0e58rLDrXI9hipYLpnPQIGVC3_-HPoRpSH85UkYZZlRx3IidqPSqGAdsLssAs0fLbLLMHi2zZ8vSyLu_j7gM_PEoAW9PgEfES1trZXKRi98jOZpj</recordid><startdate>20200501</startdate><enddate>20200501</enddate><creator>Zhang, Jiong</creator><creator>Qiao, Yuchuan</creator><creator>Sarabi, Mona Sharifi</creator><creator>Khansari, Maziyar M.</creator><creator>Gahm, Jin K.</creator><creator>Kashani, Amir H.</creator><creator>Shi, Yonggang</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>IEEE transactions on medical imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhang, Jiong</au><au>Qiao, Yuchuan</au><au>Sarabi, Mona Sharifi</au><au>Khansari, Maziyar M.</au><au>Gahm, Jin K.</au><au>Kashani, Amir H.</au><au>Shi, Yonggang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>3D Shape Modeling and Analysis of Retinal Microvasculature in OCT-Angiography Images</atitle><jtitle>IEEE transactions on medical imaging</jtitle><stitle>TMI</stitle><addtitle>IEEE Trans Med Imaging</addtitle><date>2020-05-01</date><risdate>2020</risdate><volume>39</volume><issue>5</issue><spage>1335</spage><epage>1346</epage><pages>1335-1346</pages><issn>0278-0062</issn><eissn>1558-254X</eissn><coden>ITMID4</coden><abstract>3D optical coherence tomography angiography (OCT-A) is a novel and non-invasive imaging modality for analyzing retinal diseases. The studies of microvasculature in 2D en face projection images have been widely implemented, but comprehensive 3D analysis of OCT-A images with rich depth-resolved microvascular information is rarely considered. In this paper, we propose a robust, effective, and automatic 3D shape modeling framework to provide a high-quality 3D vessel representation and to preserve valuable 3D geometric and topological information for vessel analysis. Effective vessel enhancement and extraction steps by means of curvelet denoising and optimally oriented flux (OOF) filtering are first designed to produce 3D microvascular networks. Afterwards, a novel 3D data representation of OCT-A microvasculature is reconstructed via advanced mesh reconstruction techniques. Based on the 3D surfaces, shape analysis is established to extract novel shape-based microvascular area distortion via the Laplace-Beltrami eigen-projection. The extracted feature is integrated into a graph-cut segmentation system to categorize large vessels and small capillaries for more precise shape analysis. The proposed framework is validated on a dedicated repeated scan dataset including 260 volume images and shows high repeatability. Statistical analysis using the surface area biomarker is performed on small capillaries to avoid the effect of tailing artifact from large vessels. It shows significant differences (p<; 0.001) between DR stages on 100 subjects in a OCTA-DR dataset. The proposed shape modeling and analysis framework opens the possibility for further investigating OCT-A microvasculature in a new perspective.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>31647423</pmid><doi>10.1109/TMI.2019.2948867</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-3466-4302</orcidid><orcidid>https://orcid.org/0000-0002-1922-9118</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Angiography Biomarkers Blood vessels Capillaries Datasets Diabetic retinopathy Feature extraction Finite element method Image reconstruction Image segmentation Medical imaging Microvasculature Modelling Noise reduction Optical Coherence Tomography Optical coherence tomography angiography Representations Retina retinal microvasculature Shape shape analysis Solid modeling Statistical analysis Surface reconstruction Surface treatment Three dimensional models Three-dimensional displays |
| title | 3D Shape Modeling and Analysis of Retinal Microvasculature in OCT-Angiography Images |
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