Skeletonization method for vessel delineation of arteriovenous malformation
Cerebral arteriovenous malformation (AVM) presents a great health threat due to its high probability of rupture that can cause severe brain damage. Image segmentation alone is not sufficient to support AVM embolization procedure. In order to successfully navigate the catheter and perform embolizatio...
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| Veröffentlicht in: | Computers in biology and medicine 2018-02, Vol.93, p.93-105 |
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| creator | Babin, D. Pižurica, A. Velicki, L. Matić, V. Galić, I. Leventić, H. Zlokolica, V. Philips, W. |
| description | Cerebral arteriovenous malformation (AVM) presents a great health threat due to its high probability of rupture that can cause severe brain damage. Image segmentation alone is not sufficient to support AVM embolization procedure. In order to successfully navigate the catheter and perform embolization, the segmented blood vessels need to be classified into feeding arteries, draining veins and the AVM nidus. For this reason we address here the AVM localization and vessel decomposition problem. We propose in this paper a novel AVM localization and vessel delineation method using ordered thinning-based skeletonization. The main focus of vessel delineation is the delineation of draining veins since it is essential for the embolization procedure. The main contribution is a graph-based method for exact extraction of draining veins which, in combination with our earlier work on AVM detection, allows the AVM decomposition into veins, arteries and the nidus (with an emphasis on the draining veins). We validate the proposed approach on blood vessel phantoms representing the veins and the AVM structure, as well as on cerebral 3D digital rotational angiography (3DRA) images before and after embolization, paired with digital subtraction angiography (DSA) images. Results on AVM delineation show high correspondence to the ground truth structures and indicate potentials for use in surgical planning.
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•Automatic localization and decomposition of brain arteriovenous malformation (AVM).•Image skeletonization used for automatic AVM localization and extraction.•Graph-based skeleton processing for extraction of draining veins.•Validation against ground truth segmentation, phantoms and post-embolization scans. |
| doi_str_mv | 10.1016/j.compbiomed.2017.12.011 |
| format | Article |
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[Display omitted]
•Automatic localization and decomposition of brain arteriovenous malformation (AVM).•Image skeletonization used for automatic AVM localization and extraction.•Graph-based skeleton processing for extraction of draining veins.•Validation against ground truth segmentation, phantoms and post-embolization scans.</description><identifier>ISSN: 0010-4825</identifier><identifier>EISSN: 1879-0534</identifier><identifier>DOI: 10.1016/j.compbiomed.2017.12.011</identifier><identifier>PMID: 29291536</identifier><language>eng</language><publisher>United States: Elsevier Ltd</publisher><subject>3D rotational angiography ; Algorithms ; Aneurysms ; Angiography ; Arteries ; Arteriovenous malformation delineation ; Blood vessels ; Brain damage ; Brain injury ; Catheters ; Cerebral Angiography ; Cerebral blood flow ; Decomposition ; Delineation ; Digital imaging ; Embolization ; Female ; Ground truth ; Health risks ; Humans ; Image processing ; Image Processing, Computer-Assisted - methods ; Image segmentation ; Image skeletonization ; International conferences ; Intracranial Arteriovenous Malformations - diagnostic imaging ; Localization ; Male ; Medical image analysis ; Medical imaging ; Medical instruments ; Methods ; Morphology ; Neuroimaging ; Principal components analysis ; Software ; Surgery ; Veins ; Veins & arteries ; Visualization</subject><ispartof>Computers in biology and medicine, 2018-02, Vol.93, p.93-105</ispartof><rights>2017 Elsevier Ltd</rights><rights>Copyright © 2017 Elsevier Ltd. All rights reserved.</rights><rights>Copyright Elsevier Limited Feb 1, 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c369t-199423433e20659aac70ce1daff372801de07945ab7c09079879b46e8129e1c13</citedby><cites>FETCH-LOGICAL-c369t-199423433e20659aac70ce1daff372801de07945ab7c09079879b46e8129e1c13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2007509337?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>315,781,785,3551,27929,27930,46000,64390,64394,72474</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29291536$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Babin, D.</creatorcontrib><creatorcontrib>Pižurica, A.</creatorcontrib><creatorcontrib>Velicki, L.</creatorcontrib><creatorcontrib>Matić, V.</creatorcontrib><creatorcontrib>Galić, I.</creatorcontrib><creatorcontrib>Leventić, H.</creatorcontrib><creatorcontrib>Zlokolica, V.</creatorcontrib><creatorcontrib>Philips, W.</creatorcontrib><title>Skeletonization method for vessel delineation of arteriovenous malformation</title><title>Computers in biology and medicine</title><addtitle>Comput Biol Med</addtitle><description>Cerebral arteriovenous malformation (AVM) presents a great health threat due to its high probability of rupture that can cause severe brain damage. Image segmentation alone is not sufficient to support AVM embolization procedure. In order to successfully navigate the catheter and perform embolization, the segmented blood vessels need to be classified into feeding arteries, draining veins and the AVM nidus. For this reason we address here the AVM localization and vessel decomposition problem. We propose in this paper a novel AVM localization and vessel delineation method using ordered thinning-based skeletonization. The main focus of vessel delineation is the delineation of draining veins since it is essential for the embolization procedure. The main contribution is a graph-based method for exact extraction of draining veins which, in combination with our earlier work on AVM detection, allows the AVM decomposition into veins, arteries and the nidus (with an emphasis on the draining veins). We validate the proposed approach on blood vessel phantoms representing the veins and the AVM structure, as well as on cerebral 3D digital rotational angiography (3DRA) images before and after embolization, paired with digital subtraction angiography (DSA) images. Results on AVM delineation show high correspondence to the ground truth structures and indicate potentials for use in surgical planning.
[Display omitted]
•Automatic localization and decomposition of brain arteriovenous malformation (AVM).•Image skeletonization used for automatic AVM localization and extraction.•Graph-based skeleton processing for extraction of draining veins.•Validation against ground truth segmentation, phantoms and post-embolization scans.</description><subject>3D rotational angiography</subject><subject>Algorithms</subject><subject>Aneurysms</subject><subject>Angiography</subject><subject>Arteries</subject><subject>Arteriovenous malformation delineation</subject><subject>Blood vessels</subject><subject>Brain damage</subject><subject>Brain injury</subject><subject>Catheters</subject><subject>Cerebral Angiography</subject><subject>Cerebral blood flow</subject><subject>Decomposition</subject><subject>Delineation</subject><subject>Digital imaging</subject><subject>Embolization</subject><subject>Female</subject><subject>Ground truth</subject><subject>Health risks</subject><subject>Humans</subject><subject>Image processing</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Image segmentation</subject><subject>Image skeletonization</subject><subject>International conferences</subject><subject>Intracranial Arteriovenous Malformations - diagnostic imaging</subject><subject>Localization</subject><subject>Male</subject><subject>Medical image analysis</subject><subject>Medical imaging</subject><subject>Medical instruments</subject><subject>Methods</subject><subject>Morphology</subject><subject>Neuroimaging</subject><subject>Principal components analysis</subject><subject>Software</subject><subject>Surgery</subject><subject>Veins</subject><subject>Veins & 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Med</addtitle><date>2018-02-01</date><risdate>2018</risdate><volume>93</volume><spage>93</spage><epage>105</epage><pages>93-105</pages><issn>0010-4825</issn><eissn>1879-0534</eissn><abstract>Cerebral arteriovenous malformation (AVM) presents a great health threat due to its high probability of rupture that can cause severe brain damage. Image segmentation alone is not sufficient to support AVM embolization procedure. In order to successfully navigate the catheter and perform embolization, the segmented blood vessels need to be classified into feeding arteries, draining veins and the AVM nidus. For this reason we address here the AVM localization and vessel decomposition problem. We propose in this paper a novel AVM localization and vessel delineation method using ordered thinning-based skeletonization. The main focus of vessel delineation is the delineation of draining veins since it is essential for the embolization procedure. The main contribution is a graph-based method for exact extraction of draining veins which, in combination with our earlier work on AVM detection, allows the AVM decomposition into veins, arteries and the nidus (with an emphasis on the draining veins). We validate the proposed approach on blood vessel phantoms representing the veins and the AVM structure, as well as on cerebral 3D digital rotational angiography (3DRA) images before and after embolization, paired with digital subtraction angiography (DSA) images. Results on AVM delineation show high correspondence to the ground truth structures and indicate potentials for use in surgical planning.
[Display omitted]
•Automatic localization and decomposition of brain arteriovenous malformation (AVM).•Image skeletonization used for automatic AVM localization and extraction.•Graph-based skeleton processing for extraction of draining veins.•Validation against ground truth segmentation, phantoms and post-embolization scans.</abstract><cop>United States</cop><pub>Elsevier Ltd</pub><pmid>29291536</pmid><doi>10.1016/j.compbiomed.2017.12.011</doi><tpages>13</tpages></addata></record> |
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| subjects | 3D rotational angiography Algorithms Aneurysms Angiography Arteries Arteriovenous malformation delineation Blood vessels Brain damage Brain injury Catheters Cerebral Angiography Cerebral blood flow Decomposition Delineation Digital imaging Embolization Female Ground truth Health risks Humans Image processing Image Processing, Computer-Assisted - methods Image segmentation Image skeletonization International conferences Intracranial Arteriovenous Malformations - diagnostic imaging Localization Male Medical image analysis Medical imaging Medical instruments Methods Morphology Neuroimaging Principal components analysis Software Surgery Veins Veins & arteries Visualization |
| title | Skeletonization method for vessel delineation of arteriovenous malformation |
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