Accuracy of software designed for automated localization of the inferior alveolar nerve canal on cone beam CT images
The aim of this study was to design and evaluate a new method for automated localization of the inferior alveolar nerve canal on CBCT images. The proposed method is based on traversing both panoramic and cross-sectional slices. For the panoramic slices, morphological skeletonization is imposed, and...
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| Veröffentlicht in: | Dento-maxillo-facial radiology 2016-02, Vol.45 (2), p.20150298-20150298 |
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| container_title | Dento-maxillo-facial radiology |
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| creator | Bahrampour, Ehsan Zamani, Ali Kashkouli, Sadegh Soltanimehr, Elham Ghofrani Jahromi, Mohsen Sanaeian Pourshirazi, Zahra |
| description | The aim of this study was to design and evaluate a new method for automated localization of the inferior alveolar nerve canal on CBCT images.
The proposed method is based on traversing both panoramic and cross-sectional slices. For the panoramic slices, morphological skeletonization is imposed, and a modified Hough transform is used while traversing the cross-sectional slices. A total of 40 CBCT images were randomly selected. Two experts twice located the inferior alveolar nerve canal during two examinations set 6 weeks apart. Agreement between experts was achieved, and the result of this manual technique was considered the gold standard for our study. The distances for the automated method and those determined using the gold standard method were calculated and recorded. The mean time required for the automated detection was also recorded.
The average mean distance error from the baseline was 0.75 ± 0.34 mm. In all, 86% of the detected points had a mean error of |
| doi_str_mv | 10.1259/dmfr.20150298 |
| format | Article |
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The proposed method is based on traversing both panoramic and cross-sectional slices. For the panoramic slices, morphological skeletonization is imposed, and a modified Hough transform is used while traversing the cross-sectional slices. A total of 40 CBCT images were randomly selected. Two experts twice located the inferior alveolar nerve canal during two examinations set 6 weeks apart. Agreement between experts was achieved, and the result of this manual technique was considered the gold standard for our study. The distances for the automated method and those determined using the gold standard method were calculated and recorded. The mean time required for the automated detection was also recorded.
The average mean distance error from the baseline was 0.75 ± 0.34 mm. In all, 86% of the detected points had a mean error of <1 mm compared with those determined by the manual gold standard method.
The proposed method is far more accurate and faster than previous methods. It also provides more accuracy than human annotation within a shorter time.</description><identifier>ISSN: 0250-832X</identifier><identifier>EISSN: 1476-542X</identifier><identifier>DOI: 10.1259/dmfr.20150298</identifier><identifier>PMID: 26652929</identifier><language>eng</language><publisher>England: The British Institute of Radiology</publisher><subject><![CDATA[Algorithms ; Anatomy, Cross-Sectional - statistics & numerical data ; Cone-Beam Computed Tomography - statistics & numerical data ; Dentistry ; Humans ; Image Processing, Computer-Assisted - statistics & numerical data ; Imaging, Three-Dimensional - statistics & numerical data ; Mandibular Nerve - diagnostic imaging ; Radiography, Panoramic - statistics & numerical data ; Software - statistics & numerical data ; Software Design]]></subject><ispartof>Dento-maxillo-facial radiology, 2016-02, Vol.45 (2), p.20150298-20150298</ispartof><rights>2015 The Authors. Published by the British Institute of Radiology 2015 The Authors</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c387t-53323ced2cea72610c9810b577db53da51c3266e6fd0b4c8c66b52bed38c6a323</citedby><cites>FETCH-LOGICAL-c387t-53323ced2cea72610c9810b577db53da51c3266e6fd0b4c8c66b52bed38c6a323</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26652929$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bahrampour, Ehsan</creatorcontrib><creatorcontrib>Zamani, Ali</creatorcontrib><creatorcontrib>Kashkouli, Sadegh</creatorcontrib><creatorcontrib>Soltanimehr, Elham</creatorcontrib><creatorcontrib>Ghofrani Jahromi, Mohsen</creatorcontrib><creatorcontrib>Sanaeian Pourshirazi, Zahra</creatorcontrib><title>Accuracy of software designed for automated localization of the inferior alveolar nerve canal on cone beam CT images</title><title>Dento-maxillo-facial radiology</title><addtitle>Dentomaxillofac Radiol</addtitle><description>The aim of this study was to design and evaluate a new method for automated localization of the inferior alveolar nerve canal on CBCT images.
The proposed method is based on traversing both panoramic and cross-sectional slices. For the panoramic slices, morphological skeletonization is imposed, and a modified Hough transform is used while traversing the cross-sectional slices. A total of 40 CBCT images were randomly selected. Two experts twice located the inferior alveolar nerve canal during two examinations set 6 weeks apart. Agreement between experts was achieved, and the result of this manual technique was considered the gold standard for our study. The distances for the automated method and those determined using the gold standard method were calculated and recorded. The mean time required for the automated detection was also recorded.
The average mean distance error from the baseline was 0.75 ± 0.34 mm. In all, 86% of the detected points had a mean error of <1 mm compared with those determined by the manual gold standard method.
The proposed method is far more accurate and faster than previous methods. It also provides more accuracy than human annotation within a shorter time.</description><subject>Algorithms</subject><subject>Anatomy, Cross-Sectional - statistics & numerical data</subject><subject>Cone-Beam Computed Tomography - statistics & numerical data</subject><subject>Dentistry</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted - statistics & numerical data</subject><subject>Imaging, Three-Dimensional - statistics & numerical data</subject><subject>Mandibular Nerve - diagnostic imaging</subject><subject>Radiography, Panoramic - statistics & numerical data</subject><subject>Software - statistics & numerical data</subject><subject>Software Design</subject><issn>0250-832X</issn><issn>1476-542X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVkU1rHDEMhk1paLZpj70WH3uZ1B9rz8ylEJYmLQR6SSA3o7E1GxePndqeLcmvzwz5oD1JQo9eSbyEfOLslAvVf3XTmE8F44qJvntDNnzb6kZtxc1bsmFCsaaT4uaYvC_lN2NsK5V-R46F1kr0ot-QembtnMHe0zTSksb6FzJSh8XvIzo6pkxhrmmCulQhWQj-AapPceXrLVIfR8x-xcIBU4BMI-YDUgsRAl04myLSAWGiuyvqJ9hj-UCORggFPz7HE3J9_v1q96O5_HXxc3d22VjZtbVRUgpp0QmL0ArNme07zgbVtm5Q0oHiVi6foB4dG7a2s1oPSgzo5JLCMntCvj3p3s3DhM5irBmCucvLGfneJPDm_070t2afDkaxTvZKLwJfngVy-jNjqWbyxWIIEDHNxfBWs65nPVvR5gm1OZWScXxdw5lZnTKrU-bFqYX__O9tr_SLNfIRiG6Scg</recordid><startdate>20160201</startdate><enddate>20160201</enddate><creator>Bahrampour, Ehsan</creator><creator>Zamani, Ali</creator><creator>Kashkouli, Sadegh</creator><creator>Soltanimehr, Elham</creator><creator>Ghofrani Jahromi, Mohsen</creator><creator>Sanaeian Pourshirazi, Zahra</creator><general>The British Institute of Radiology</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20160201</creationdate><title>Accuracy of software designed for automated localization of the inferior alveolar nerve canal on cone beam CT images</title><author>Bahrampour, Ehsan ; Zamani, Ali ; Kashkouli, Sadegh ; Soltanimehr, Elham ; Ghofrani Jahromi, Mohsen ; Sanaeian Pourshirazi, Zahra</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c387t-53323ced2cea72610c9810b577db53da51c3266e6fd0b4c8c66b52bed38c6a323</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Algorithms</topic><topic>Anatomy, Cross-Sectional - statistics & numerical data</topic><topic>Cone-Beam Computed Tomography - statistics & numerical data</topic><topic>Dentistry</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted - statistics & numerical data</topic><topic>Imaging, Three-Dimensional - statistics & numerical data</topic><topic>Mandibular Nerve - diagnostic imaging</topic><topic>Radiography, Panoramic - statistics & numerical data</topic><topic>Software - statistics & numerical data</topic><topic>Software Design</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bahrampour, Ehsan</creatorcontrib><creatorcontrib>Zamani, Ali</creatorcontrib><creatorcontrib>Kashkouli, Sadegh</creatorcontrib><creatorcontrib>Soltanimehr, Elham</creatorcontrib><creatorcontrib>Ghofrani Jahromi, Mohsen</creatorcontrib><creatorcontrib>Sanaeian Pourshirazi, Zahra</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Dento-maxillo-facial radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bahrampour, Ehsan</au><au>Zamani, Ali</au><au>Kashkouli, Sadegh</au><au>Soltanimehr, Elham</au><au>Ghofrani Jahromi, Mohsen</au><au>Sanaeian Pourshirazi, Zahra</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Accuracy of software designed for automated localization of the inferior alveolar nerve canal on cone beam CT images</atitle><jtitle>Dento-maxillo-facial radiology</jtitle><addtitle>Dentomaxillofac Radiol</addtitle><date>2016-02-01</date><risdate>2016</risdate><volume>45</volume><issue>2</issue><spage>20150298</spage><epage>20150298</epage><pages>20150298-20150298</pages><issn>0250-832X</issn><eissn>1476-542X</eissn><abstract>The aim of this study was to design and evaluate a new method for automated localization of the inferior alveolar nerve canal on CBCT images.
The proposed method is based on traversing both panoramic and cross-sectional slices. For the panoramic slices, morphological skeletonization is imposed, and a modified Hough transform is used while traversing the cross-sectional slices. A total of 40 CBCT images were randomly selected. Two experts twice located the inferior alveolar nerve canal during two examinations set 6 weeks apart. Agreement between experts was achieved, and the result of this manual technique was considered the gold standard for our study. The distances for the automated method and those determined using the gold standard method were calculated and recorded. The mean time required for the automated detection was also recorded.
The average mean distance error from the baseline was 0.75 ± 0.34 mm. In all, 86% of the detected points had a mean error of <1 mm compared with those determined by the manual gold standard method.
The proposed method is far more accurate and faster than previous methods. It also provides more accuracy than human annotation within a shorter time.</abstract><cop>England</cop><pub>The British Institute of Radiology</pub><pmid>26652929</pmid><doi>10.1259/dmfr.20150298</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| issn | 0250-832X 1476-542X |
| language | eng |
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| source | MEDLINE; Oxford University Press Journals All Titles (1996-Current); EZB-FREE-00999 freely available EZB journals |
| subjects | Algorithms Anatomy, Cross-Sectional - statistics & numerical data Cone-Beam Computed Tomography - statistics & numerical data Dentistry Humans Image Processing, Computer-Assisted - statistics & numerical data Imaging, Three-Dimensional - statistics & numerical data Mandibular Nerve - diagnostic imaging Radiography, Panoramic - statistics & numerical data Software - statistics & numerical data Software Design |
| title | Accuracy of software designed for automated localization of the inferior alveolar nerve canal on cone beam CT images |
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