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Automatic Coronary Artery Segmentation Using Active Search for Branches and Seemingly Disconnected Vessel Segments from Coronary CT Angiography
We propose a Bayesian tracking and segmentation method of coronary arteries on coronary computed tomographic angiography (CCTA). The geometry of coronary arteries including lumen boundary is estimated in Maximum A Posteriori (MAP) framework. Three consecutive sphere based filtering is combined with...
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Published in: | PloS one 2016-08, Vol.11 (8), p.e0156837-e0156837 |
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description | We propose a Bayesian tracking and segmentation method of coronary arteries on coronary computed tomographic angiography (CCTA). The geometry of coronary arteries including lumen boundary is estimated in Maximum A Posteriori (MAP) framework. Three consecutive sphere based filtering is combined with a stochastic process that is based on the similarity of the consecutive local neighborhood voxels and the geometric constraint of a vessel. It is also founded on the prior knowledge that an artery can be seen locally disconnected and consist of branches which may be seemingly disconnected due to plaque build up. For such problem, an active search method is proposed to find branches and seemingly disconnected but actually connected vessel segments. Several new measures have been developed for branch detection, disconnection check and planar vesselness measure. Using public domain Rotterdam CT dataset, the accuracy of extracted centerline is demonstrated and automatic reconstruction of coronary artery mesh is shown. |
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Using public domain Rotterdam CT dataset, the accuracy of extracted centerline is demonstrated and automatic reconstruction of coronary artery mesh is shown.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0156837</identifier><identifier>PMID: 27536939</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Angiography ; Arteries ; Automation ; Bayes Theorem ; Bayesian analysis ; Biology and Life Sciences ; Cardiovascular disease ; Computed tomography ; Computed Tomography Angiography - methods ; Coronary Angiography - methods ; Coronary arteries ; Coronary artery ; Coronary heart disease ; Coronary vessels ; Coronary Vessels - anatomy & histology ; Coronary Vessels - diagnostic imaging ; Diagnosis ; Engineering and Technology ; Filtration ; Geometry ; Humans ; International conferences ; Medical imaging ; Medicine ; Medicine and Health Sciences ; Methods ; Models, Theoretical ; Neural networks ; Physical Sciences ; Public domain ; Research and Analysis Methods ; Segmentation ; Segments ; Stochastic Processes ; Stochasticity</subject><ispartof>PloS one, 2016-08, Vol.11 (8), p.e0156837-e0156837</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>2016 Han et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2016 Han et al 2016 Han et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c725t-4f45281f9974ec6214820cc8071daf0f75caf1c0d38939ce64fe3f49a95c65213</citedby><cites>FETCH-LOGICAL-c725t-4f45281f9974ec6214820cc8071daf0f75caf1c0d38939ce64fe3f49a95c65213</cites><orcidid>0000-0003-1093-1741</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1812824103/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1812824103?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,882,25734,27905,27906,36993,36994,44571,53772,53774,74875</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27536939$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Zeng, Li</contributor><creatorcontrib>Han, Dongjin</creatorcontrib><creatorcontrib>Shim, Hackjoon</creatorcontrib><creatorcontrib>Jeon, Byunghwan</creatorcontrib><creatorcontrib>Jang, Yeonggul</creatorcontrib><creatorcontrib>Hong, Youngtaek</creatorcontrib><creatorcontrib>Jung, Sunghee</creatorcontrib><creatorcontrib>Ha, Seongmin</creatorcontrib><creatorcontrib>Chang, Hyuk-Jae</creatorcontrib><title>Automatic Coronary Artery Segmentation Using Active Search for Branches and Seemingly Disconnected Vessel Segments from Coronary CT Angiography</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>We propose a Bayesian tracking and segmentation method of coronary arteries on coronary computed tomographic angiography (CCTA). The geometry of coronary arteries including lumen boundary is estimated in Maximum A Posteriori (MAP) framework. Three consecutive sphere based filtering is combined with a stochastic process that is based on the similarity of the consecutive local neighborhood voxels and the geometric constraint of a vessel. It is also founded on the prior knowledge that an artery can be seen locally disconnected and consist of branches which may be seemingly disconnected due to plaque build up. For such problem, an active search method is proposed to find branches and seemingly disconnected but actually connected vessel segments. Several new measures have been developed for branch detection, disconnection check and planar vesselness measure. Using public domain Rotterdam CT dataset, the accuracy of extracted centerline is demonstrated and automatic reconstruction of coronary artery mesh is shown.</description><subject>Algorithms</subject><subject>Angiography</subject><subject>Arteries</subject><subject>Automation</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Biology and Life Sciences</subject><subject>Cardiovascular disease</subject><subject>Computed tomography</subject><subject>Computed Tomography Angiography - methods</subject><subject>Coronary Angiography - methods</subject><subject>Coronary arteries</subject><subject>Coronary artery</subject><subject>Coronary heart disease</subject><subject>Coronary vessels</subject><subject>Coronary Vessels - anatomy & histology</subject><subject>Coronary Vessels - diagnostic imaging</subject><subject>Diagnosis</subject><subject>Engineering and 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Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Han, Dongjin</au><au>Shim, Hackjoon</au><au>Jeon, Byunghwan</au><au>Jang, Yeonggul</au><au>Hong, Youngtaek</au><au>Jung, Sunghee</au><au>Ha, Seongmin</au><au>Chang, Hyuk-Jae</au><au>Zeng, Li</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automatic Coronary Artery Segmentation Using Active Search for Branches and Seemingly Disconnected Vessel Segments from Coronary CT Angiography</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2016-08-18</date><risdate>2016</risdate><volume>11</volume><issue>8</issue><spage>e0156837</spage><epage>e0156837</epage><pages>e0156837-e0156837</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>We propose a Bayesian tracking and segmentation method of coronary arteries on coronary computed tomographic angiography (CCTA). The geometry of coronary arteries including lumen boundary is estimated in Maximum A Posteriori (MAP) framework. Three consecutive sphere based filtering is combined with a stochastic process that is based on the similarity of the consecutive local neighborhood voxels and the geometric constraint of a vessel. It is also founded on the prior knowledge that an artery can be seen locally disconnected and consist of branches which may be seemingly disconnected due to plaque build up. For such problem, an active search method is proposed to find branches and seemingly disconnected but actually connected vessel segments. Several new measures have been developed for branch detection, disconnection check and planar vesselness measure. Using public domain Rotterdam CT dataset, the accuracy of extracted centerline is demonstrated and automatic reconstruction of coronary artery mesh is shown.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>27536939</pmid><doi>10.1371/journal.pone.0156837</doi><tpages>e0156837</tpages><orcidid>https://orcid.org/0000-0003-1093-1741</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Angiography Arteries Automation Bayes Theorem Bayesian analysis Biology and Life Sciences Cardiovascular disease Computed tomography Computed Tomography Angiography - methods Coronary Angiography - methods Coronary arteries Coronary artery Coronary heart disease Coronary vessels Coronary Vessels - anatomy & histology Coronary Vessels - diagnostic imaging Diagnosis Engineering and Technology Filtration Geometry Humans International conferences Medical imaging Medicine Medicine and Health Sciences Methods Models, Theoretical Neural networks Physical Sciences Public domain Research and Analysis Methods Segmentation Segments Stochastic Processes Stochasticity |
title | Automatic Coronary Artery Segmentation Using Active Search for Branches and Seemingly Disconnected Vessel Segments from Coronary CT Angiography |
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