Automated vertebrae localization and identification by decision forests and image-based refinement on real-world CT data
Purpose Development of a fully automatic algorithm for the automatic localization and identification of vertebral bodies in computed tomography (CT). Materials and methods This algorithm was developed using a dataset based on real-world data of 232 thoraco-abdominopelvic CT scans retrospectively col...
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| Veröffentlicht in: | Radiologia medica 2020-01, Vol.125 (1), p.48-56 |
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| container_title | Radiologia medica |
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| creator | Jimenez-Pastor, Ana Alberich-Bayarri, Angel Fos-Guarinos, Belen Garcia-Castro, Fabio Garcia-Juan, David Glocker, Ben Marti-Bonmati, Luis |
| description | Purpose
Development of a fully automatic algorithm for the automatic localization and identification of vertebral bodies in computed tomography (CT).
Materials and methods
This algorithm was developed using a dataset based on real-world data of 232 thoraco-abdominopelvic CT scans retrospectively collected. In order to achieve an accurate solution, a two-stage automated method was developed: decision forests for a rough prediction of vertebral bodies position, and morphological image processing techniques to refine the previous detection by locating the position of the spinal canal.
Results
The mean distance error between the predicted vertebrae centroid position and truth was 13.7 mm. The identification rate was 79.6% on the thoracic region and of 74.8% on the lumbar segment.
Conclusion
The algorithm provides a new method to detect and identify vertebral bodies from arbitrary field-of-view body CT scans. |
| doi_str_mv | 10.1007/s11547-019-01079-9 |
| format | Article |
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Development of a fully automatic algorithm for the automatic localization and identification of vertebral bodies in computed tomography (CT).
Materials and methods
This algorithm was developed using a dataset based on real-world data of 232 thoraco-abdominopelvic CT scans retrospectively collected. In order to achieve an accurate solution, a two-stage automated method was developed: decision forests for a rough prediction of vertebral bodies position, and morphological image processing techniques to refine the previous detection by locating the position of the spinal canal.
Results
The mean distance error between the predicted vertebrae centroid position and truth was 13.7 mm. The identification rate was 79.6% on the thoracic region and of 74.8% on the lumbar segment.
Conclusion
The algorithm provides a new method to detect and identify vertebral bodies from arbitrary field-of-view body CT scans.</description><identifier>ISSN: 0033-8362</identifier><identifier>EISSN: 1826-6983</identifier><identifier>DOI: 10.1007/s11547-019-01079-9</identifier><identifier>PMID: 31522345</identifier><language>eng</language><publisher>Milan: Springer Milan</publisher><subject>Adult ; Aged ; Aged, 80 and over ; Algorithms ; Anatomic Landmarks - diagnostic imaging ; Automation ; Centroids ; Computed tomography ; Computer Application ; Datasets as Topic ; Decision Trees ; Diagnostic Radiology ; Field of view ; Forest management ; Humans ; Identification methods ; Image detection ; Image processing ; Imaging ; Interventional Radiology ; Localization ; Machine Learning ; Medical imaging ; Medicine ; Medicine & Public Health ; Middle Aged ; Multidetector Computed Tomography - methods ; Neuroradiology ; Predictions ; Radiology ; Retrospective Studies ; Spine - diagnostic imaging ; Ultrasound ; Vertebrae ; Young Adult</subject><ispartof>Radiologia medica, 2020-01, Vol.125 (1), p.48-56</ispartof><rights>Italian Society of Medical Radiology 2019</rights><rights>Copyright Springer Nature B.V. 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c441t-1539fb4f5f2d7e80daff2ec6591f02dd6b9a50363f79413ed693b3fa5990ca1e3</citedby><cites>FETCH-LOGICAL-c441t-1539fb4f5f2d7e80daff2ec6591f02dd6b9a50363f79413ed693b3fa5990ca1e3</cites><orcidid>0000-0002-0978-9429</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11547-019-01079-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11547-019-01079-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31522345$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Jimenez-Pastor, Ana</creatorcontrib><creatorcontrib>Alberich-Bayarri, Angel</creatorcontrib><creatorcontrib>Fos-Guarinos, Belen</creatorcontrib><creatorcontrib>Garcia-Castro, Fabio</creatorcontrib><creatorcontrib>Garcia-Juan, David</creatorcontrib><creatorcontrib>Glocker, Ben</creatorcontrib><creatorcontrib>Marti-Bonmati, Luis</creatorcontrib><title>Automated vertebrae localization and identification by decision forests and image-based refinement on real-world CT data</title><title>Radiologia medica</title><addtitle>Radiol med</addtitle><addtitle>Radiol Med</addtitle><description>Purpose
Development of a fully automatic algorithm for the automatic localization and identification of vertebral bodies in computed tomography (CT).
Materials and methods
This algorithm was developed using a dataset based on real-world data of 232 thoraco-abdominopelvic CT scans retrospectively collected. In order to achieve an accurate solution, a two-stage automated method was developed: decision forests for a rough prediction of vertebral bodies position, and morphological image processing techniques to refine the previous detection by locating the position of the spinal canal.
Results
The mean distance error between the predicted vertebrae centroid position and truth was 13.7 mm. The identification rate was 79.6% on the thoracic region and of 74.8% on the lumbar segment.
Conclusion
The algorithm provides a new method to detect and identify vertebral bodies from arbitrary field-of-view body CT scans.</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Algorithms</subject><subject>Anatomic Landmarks - diagnostic imaging</subject><subject>Automation</subject><subject>Centroids</subject><subject>Computed tomography</subject><subject>Computer Application</subject><subject>Datasets as Topic</subject><subject>Decision Trees</subject><subject>Diagnostic Radiology</subject><subject>Field of view</subject><subject>Forest management</subject><subject>Humans</subject><subject>Identification methods</subject><subject>Image detection</subject><subject>Image processing</subject><subject>Imaging</subject><subject>Interventional Radiology</subject><subject>Localization</subject><subject>Machine Learning</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Multidetector Computed Tomography - methods</subject><subject>Neuroradiology</subject><subject>Predictions</subject><subject>Radiology</subject><subject>Retrospective Studies</subject><subject>Spine - diagnostic imaging</subject><subject>Ultrasound</subject><subject>Vertebrae</subject><subject>Young Adult</subject><issn>0033-8362</issn><issn>1826-6983</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kUtLxDAUhYMozjj6B1xIwY2baB5N2yyHwRcMuBnXIW1uhkrbaNKq4683Y0cFFy5Cws13zr2Xg9ApJZeUkPwqUCrSHBMq4yG5xHIPTWnBMpzJgu-jKSGc44JnbIKOQngiJI2cPEQTTgVjPBVT9D4fetfqHkzyCr6H0mtIGlfppv7Qfe26RHcmqQ10fW3raiyVm8RAVYft2zoPoQ8j1uo14FKH6ObB1h20UZdEyoNu8JvzjUkWq8ToXh-jA6ubACe7e4Yeb65Xizu8fLi9X8yXuEpT2mMquLRlaoVlJoeCGG0tgyoTklrCjMlKqQXhGbe5TCkHk0lecquFlKTSFPgMXYy-z969DHFU1dahgqbRHbghKMYkkUUqBI3o-R_0yQ2-i9MpxjmLTbKCRYqNVOVdCHFN9ezj4n6jKFHbXNSYi4q5qK9clIyis531ULZgfiTfQUSAj0CIX90a_G_vf2w_AfX1mbg</recordid><startdate>20200101</startdate><enddate>20200101</enddate><creator>Jimenez-Pastor, Ana</creator><creator>Alberich-Bayarri, Angel</creator><creator>Fos-Guarinos, Belen</creator><creator>Garcia-Castro, Fabio</creator><creator>Garcia-Juan, David</creator><creator>Glocker, Ben</creator><creator>Marti-Bonmati, Luis</creator><general>Springer Milan</general><general>Springer Nature B.V</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><orcidid>https://orcid.org/0000-0002-0978-9429</orcidid></search><sort><creationdate>20200101</creationdate><title>Automated vertebrae localization and identification by decision forests and image-based refinement on real-world CT data</title><author>Jimenez-Pastor, Ana ; Alberich-Bayarri, Angel ; Fos-Guarinos, Belen ; Garcia-Castro, Fabio ; Garcia-Juan, David ; Glocker, Ben ; Marti-Bonmati, Luis</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c441t-1539fb4f5f2d7e80daff2ec6591f02dd6b9a50363f79413ed693b3fa5990ca1e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Algorithms</topic><topic>Anatomic Landmarks - diagnostic imaging</topic><topic>Automation</topic><topic>Centroids</topic><topic>Computed tomography</topic><topic>Computer Application</topic><topic>Datasets as Topic</topic><topic>Decision Trees</topic><topic>Diagnostic Radiology</topic><topic>Field of view</topic><topic>Forest management</topic><topic>Humans</topic><topic>Identification methods</topic><topic>Image detection</topic><topic>Image processing</topic><topic>Imaging</topic><topic>Interventional Radiology</topic><topic>Localization</topic><topic>Machine Learning</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Multidetector Computed Tomography - methods</topic><topic>Neuroradiology</topic><topic>Predictions</topic><topic>Radiology</topic><topic>Retrospective Studies</topic><topic>Spine - diagnostic imaging</topic><topic>Ultrasound</topic><topic>Vertebrae</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jimenez-Pastor, Ana</creatorcontrib><creatorcontrib>Alberich-Bayarri, Angel</creatorcontrib><creatorcontrib>Fos-Guarinos, Belen</creatorcontrib><creatorcontrib>Garcia-Castro, Fabio</creatorcontrib><creatorcontrib>Garcia-Juan, David</creatorcontrib><creatorcontrib>Glocker, Ben</creatorcontrib><creatorcontrib>Marti-Bonmati, Luis</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><jtitle>Radiologia medica</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jimenez-Pastor, Ana</au><au>Alberich-Bayarri, Angel</au><au>Fos-Guarinos, Belen</au><au>Garcia-Castro, Fabio</au><au>Garcia-Juan, David</au><au>Glocker, Ben</au><au>Marti-Bonmati, Luis</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automated vertebrae localization and identification by decision forests and image-based refinement on real-world CT data</atitle><jtitle>Radiologia medica</jtitle><stitle>Radiol med</stitle><addtitle>Radiol Med</addtitle><date>2020-01-01</date><risdate>2020</risdate><volume>125</volume><issue>1</issue><spage>48</spage><epage>56</epage><pages>48-56</pages><issn>0033-8362</issn><eissn>1826-6983</eissn><abstract>Purpose
Development of a fully automatic algorithm for the automatic localization and identification of vertebral bodies in computed tomography (CT).
Materials and methods
This algorithm was developed using a dataset based on real-world data of 232 thoraco-abdominopelvic CT scans retrospectively collected. In order to achieve an accurate solution, a two-stage automated method was developed: decision forests for a rough prediction of vertebral bodies position, and morphological image processing techniques to refine the previous detection by locating the position of the spinal canal.
Results
The mean distance error between the predicted vertebrae centroid position and truth was 13.7 mm. The identification rate was 79.6% on the thoracic region and of 74.8% on the lumbar segment.
Conclusion
The algorithm provides a new method to detect and identify vertebral bodies from arbitrary field-of-view body CT scans.</abstract><cop>Milan</cop><pub>Springer Milan</pub><pmid>31522345</pmid><doi>10.1007/s11547-019-01079-9</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-0978-9429</orcidid></addata></record> |
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| subjects | Adult Aged Aged, 80 and over Algorithms Anatomic Landmarks - diagnostic imaging Automation Centroids Computed tomography Computer Application Datasets as Topic Decision Trees Diagnostic Radiology Field of view Forest management Humans Identification methods Image detection Image processing Imaging Interventional Radiology Localization Machine Learning Medical imaging Medicine Medicine & Public Health Middle Aged Multidetector Computed Tomography - methods Neuroradiology Predictions Radiology Retrospective Studies Spine - diagnostic imaging Ultrasound Vertebrae Young Adult |
| title | Automated vertebrae localization and identification by decision forests and image-based refinement on real-world CT data |
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