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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Fully automated segmentation of cartilage from the MR images of knee using a multi-atlas and local structural analysis method</title><author><name sortKey="Lee, June Goo" sort="Lee, June Goo" uniqKey="Lee J" first="June-Goo" last="Lee">June-Goo Lee</name></author><author><name sortKey="Gumus, Serter" sort="Gumus, Serter" uniqKey="Gumus S" first="Serter" last="Gumus">Serter Gumus</name></author><author><name sortKey="Moon, Chan Hong" sort="Moon, Chan Hong" uniqKey="Moon C" first="Chan Hong" last="Moon">Chan Hong Moon</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">25186408</idno><idno type="pmc">4149695</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4149695</idno><idno type="RBID">PMC:4149695</idno><idno type="doi">10.1118/1.4893533</idno><date when="2014">2014</date><idno type="wicri:Area/Pmc/Corpus">000B45</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000B45</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Fully automated segmentation of cartilage from the MR images of knee using a multi-atlas and local structural analysis method</title><author><name sortKey="Lee, June Goo" sort="Lee, June Goo" uniqKey="Lee J" first="June-Goo" last="Lee">June-Goo Lee</name></author><author><name sortKey="Gumus, Serter" sort="Gumus, Serter" uniqKey="Gumus S" first="Serter" last="Gumus">Serter Gumus</name></author><author><name sortKey="Moon, Chan Hong" sort="Moon, Chan Hong" uniqKey="Moon C" first="Chan Hong" last="Moon">Chan Hong Moon</name></author></analytic><series><title level="j">Medical Physics</title><idno type="ISSN">0094-2405</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Purpose:</title><p>To develop a fully automated method to segment cartilage from the magnetic resonance (MR) images of knee and to evaluate the performance of the method on a public, open dataset.</p></sec><sec><title>Methods:</title><p>The segmentation scheme consisted of three procedures: multiple-atlas building, applying a locally weighted vote (LWV), and region adjustment. In the atlas building procedure, all training cases were registered to a target image by a nonrigid registration scheme and the best matched atlases selected. A LWV algorithm was applied to merge the information from these atlases and generate the initial segmentation result. Subsequently, for the region adjustment procedure, the statistical information of bone, cartilage, and surrounding regions was computed from the initial segmentation result. The statistical information directed the automated determination of the seed points inside and outside bone regions for the graph-cut based method. Finally, the region adjustment was conducted by the revision of outliers and the inclusion of abnormal bone regions.</p></sec><sec><title>Results:</title><p>A total of 150 knee MR images from a public, open dataset (available at<ext-link ext-link-type="uri" xlink:href="http://www.ski10.org">www.ski10.org</ext-link>) were used for the development and evaluation of this approach. The 150 cases were divided into the training set (100 cases) and the test set (50 cases). The cartilages were segmented successfully in all test cases in an average of 40 min computation time. The average dice similarity coefficient was 71.7% ± 8.0% for femoral and 72.4% ± 6.9% for tibial cartilage.</p></sec><sec><title>Conclusions:</title><p>The authors have developed a fully automated segmentation program for knee cartilage from MR images. The performance of the program based on 50 test cases was highly promising.</p></sec></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Med Phys</journal-id><journal-id journal-id-type="iso-abbrev">Med Phys</journal-id><journal-id journal-id-type="coden">MPHYA6</journal-id><journal-title-group><journal-title>Medical Physics</journal-title></journal-title-group><issn pub-type="ppub">0094-2405</issn><publisher><publisher-name>American Association of Physicists in Medicine</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">25186408</article-id><article-id pub-id-type="pmc">4149695</article-id><article-id pub-id-type="publisher-id">044409MPH</article-id><article-id pub-id-type="publisher-id">1.4893533</article-id><article-id pub-id-type="doi">10.1118/1.4893533</article-id><article-id pub-id-type="publisher-manuscript">13-1272R1</article-id><article-categories><subj-group subj-group-type="heading"><subject>Magnetic Resonance Physics</subject></subj-group></article-categories><title-group><article-title>Fully automated segmentation of cartilage from the MR images of knee using a multi-atlas and local structural analysis method</article-title><alt-title alt-title-type="short-title">Multi-atlas based automatic cartilage segmentation</alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Lee</surname><given-names>June-Goo</given-names></name></contrib><contrib contrib-type="author"><name><surname>Gumus</surname><given-names>Serter</given-names></name></contrib><contrib contrib-type="author"><name><surname>Moon</surname><given-names>Chan Hong</given-names></name></contrib><aff>Department of Radiology,<institution>University of Pittsburgh</institution>, Pittsburgh, Pennsylvania 15213</aff></contrib-group><contrib-group><contrib contrib-type="author"><name><surname>Kwoh</surname><given-names>C. Kent</given-names></name></contrib><aff>Division of Rheumatology,<institution>University of Arizona Arthritis Center</institution>, Tucson, Arizona 85716</aff></contrib-group><contrib-group><contrib contrib-type="author"><name><surname>Bae</surname><given-names>Kyongtae Ty</given-names></name><xref ref-type="author-note" rid="n1">a)</xref></contrib><aff>Department of Radiology,<institution>University of Pittsburgh</institution>, Pittsburgh, Pennsylvania 15213</aff></contrib-group><author-notes><fn id="n1"><label>a)</label><p>Author to whom correspondence should be addressed. Electronic mail: <email>baek@upmc.edu</email></p></fn></author-notes><pub-date pub-type="ppub"><month>9</month><year>2014</year></pub-date><pub-date pub-type="epub"><day>27</day><month>8</month><year>2014</year></pub-date><volume>41</volume><issue>9</issue><elocation-id seq="1">092303</elocation-id><history><date date-type="received"><day>30</day><month>8</month><year>2013</year></date><date date-type="rev-recd"><day>19</day><month>6</month><year>2014</year></date><date date-type="accepted"><day>08</day><month>8</month><year>2014</year></date></history><permissions><copyright-statement>Copyright © 2014 American Association of Physicists in Medicine</copyright-statement><copyright-year>2014</copyright-year><copyright-holder>American Association of Physicists in Medicine</copyright-holder><license license-type="ccc"><license-p>0094-2405/2014/41(9)/092303/9/<price>$30.00</price></license-p></license></permissions><abstract><sec><title>Purpose:</title><p>To develop a fully automated method to segment cartilage from the magnetic resonance (MR) images of knee and to evaluate the performance of the method on a public, open dataset.</p></sec><sec><title>Methods:</title><p>The segmentation scheme consisted of three procedures: multiple-atlas building, applying a locally weighted vote (LWV), and region adjustment. In the atlas building procedure, all training cases were registered to a target image by a nonrigid registration scheme and the best matched atlases selected. A LWV algorithm was applied to merge the information from these atlases and generate the initial segmentation result. Subsequently, for the region adjustment procedure, the statistical information of bone, cartilage, and surrounding regions was computed from the initial segmentation result. The statistical information directed the automated determination of the seed points inside and outside bone regions for the graph-cut based method. Finally, the region adjustment was conducted by the revision of outliers and the inclusion of abnormal bone regions.</p></sec><sec><title>Results:</title><p>A total of 150 knee MR images from a public, open dataset (available at<ext-link ext-link-type="uri" xlink:href="http://www.ski10.org">www.ski10.org</ext-link>) were used for the development and evaluation of this approach. The 150 cases were divided into the training set (100 cases) and the test set (50 cases). The cartilages were segmented successfully in all test cases in an average of 40 min computation time. The average dice similarity coefficient was 71.7% ± 8.0% for femoral and 72.4% ± 6.9% for tibial cartilage.</p></sec><sec><title>Conclusions:</title><p>The authors have developed a fully automated segmentation program for knee cartilage from MR images. The performance of the program based on 50 test cases was highly promising.</p></sec></abstract><kwd-group kwd-group-type="aapm"><kwd>osteoarthritis</kwd><kwd>cartilage</kwd><kwd>multi-atlas segmentation</kwd><kwd>knee magnetic resonance imaging</kwd></kwd-group><funding-group specific-use="FundRef"><award-group><funding-source><named-content content-type="funder_name">Radiological Society of North America (RSNA)</named-content><named-content content-type="funder_identifier">http://dx.doi.org/10.13039/100006098</named-content></funding-source><award-id award-type="contract">RSD1320</award-id></award-group></funding-group><counts><page-count count="9"></page-count></counts><custom-meta-group><custom-meta><meta-name>crossmark</meta-name><meta-value></meta-value></custom-meta></custom-meta-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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