On measuring the change in size of pulmonary nodules
The pulmonary nodule is the most common manifestation of lung cancer, the most deadly of all cancers. Most small pulmonary nodules are benign, however, and currently the growth rate of the nodule provides for one of the most accurate noninvasive methods of determining malignancy. In this paper, we p...
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| Veröffentlicht in: | IEEE transactions on medical imaging 2006-04, Vol.25 (4), p.435-450 |
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| container_title | IEEE transactions on medical imaging |
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| creator | Reeves, A.P. Chan, A.B. Yankelevitz, D.F. Henschke, C.I. Kressler, B. Kostis, W.J. |
| description | The pulmonary nodule is the most common manifestation of lung cancer, the most deadly of all cancers. Most small pulmonary nodules are benign, however, and currently the growth rate of the nodule provides for one of the most accurate noninvasive methods of determining malignancy. In this paper, we present methods for measuring the change in nodule size from two computed tomography image scans recorded at different times; from this size change the growth rate may be established. The impact of partial voxels for small nodules is evaluated and isotropic resampling is shown to improve measurement accuracy. Methods for nodule location and sizing, pleural segmentation, adaptive thresholding, image registration, and knowledge-based shape matching are presented. The latter three techniques provide for a significant improvement in volume change measurement accuracy by considering both image scans simultaneously. Improvements in segmentation are evaluated by measuring volume changes in benign or slow growing nodules. In the analysis of 50 nodules, the variance in percent volume change was reduced from 11.54% to 9.35% (p=0.03) through the use of registration, adaptive thresholding, and knowledge-based shape matching. |
| doi_str_mv | 10.1109/TMI.2006.871548 |
| format | Article |
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Most small pulmonary nodules are benign, however, and currently the growth rate of the nodule provides for one of the most accurate noninvasive methods of determining malignancy. In this paper, we present methods for measuring the change in nodule size from two computed tomography image scans recorded at different times; from this size change the growth rate may be established. The impact of partial voxels for small nodules is evaluated and isotropic resampling is shown to improve measurement accuracy. Methods for nodule location and sizing, pleural segmentation, adaptive thresholding, image registration, and knowledge-based shape matching are presented. The latter three techniques provide for a significant improvement in volume change measurement accuracy by considering both image scans simultaneously. Improvements in segmentation are evaluated by measuring volume changes in benign or slow growing nodules. In the analysis of 50 nodules, the variance in percent volume change was reduced from 11.54% to 9.35% (p=0.03) through the use of registration, adaptive thresholding, and knowledge-based shape matching.</description><identifier>ISSN: 0278-0062</identifier><identifier>EISSN: 1558-254X</identifier><identifier>DOI: 10.1109/TMI.2006.871548</identifier><identifier>PMID: 16608059</identifier><identifier>CODEN: ITMID4</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Algorithms ; Artificial Intelligence ; Biopsy ; Cancer ; Computed tomography ; Growth rate ; growth rate estimation ; Humans ; Image registration ; Image segmentation ; Imaging, Three-Dimensional - methods ; Information Storage and Retrieval - methods ; Lungs ; Pattern Recognition, Automated - methods ; Phantoms, Imaging ; pulmonary nodules ; Radiographic Image Enhancement - methods ; Radiographic Image Interpretation, Computer-Assisted - methods ; Radiography ; Radiography, Thoracic - instrumentation ; Radiography, Thoracic - methods ; Reproducibility of Results ; rule-based segmentation ; Sensitivity and Specificity ; Severity of Illness Index ; Shape ; Size measurement ; Solitary Pulmonary Nodule - diagnostic imaging ; Studies ; Subtraction Technique ; Tomography, X-Ray Computed - instrumentation ; Tomography, X-Ray Computed - methods ; Volume measurement</subject><ispartof>IEEE transactions on medical imaging, 2006-04, Vol.25 (4), p.435-450</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2006</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c502t-a96b61b2a30b69b18d5e7a8c978e59ffe99184bcd0c60faa4332fc64176638c03</citedby><cites>FETCH-LOGICAL-c502t-a96b61b2a30b69b18d5e7a8c978e59ffe99184bcd0c60faa4332fc64176638c03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1610748$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1610748$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16608059$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Reeves, A.P.</creatorcontrib><creatorcontrib>Chan, A.B.</creatorcontrib><creatorcontrib>Yankelevitz, D.F.</creatorcontrib><creatorcontrib>Henschke, C.I.</creatorcontrib><creatorcontrib>Kressler, B.</creatorcontrib><creatorcontrib>Kostis, W.J.</creatorcontrib><title>On measuring the change in size of pulmonary nodules</title><title>IEEE transactions on medical imaging</title><addtitle>TMI</addtitle><addtitle>IEEE Trans Med Imaging</addtitle><description>The pulmonary nodule is the most common manifestation of lung cancer, the most deadly of all cancers. Most small pulmonary nodules are benign, however, and currently the growth rate of the nodule provides for one of the most accurate noninvasive methods of determining malignancy. In this paper, we present methods for measuring the change in nodule size from two computed tomography image scans recorded at different times; from this size change the growth rate may be established. The impact of partial voxels for small nodules is evaluated and isotropic resampling is shown to improve measurement accuracy. Methods for nodule location and sizing, pleural segmentation, adaptive thresholding, image registration, and knowledge-based shape matching are presented. The latter three techniques provide for a significant improvement in volume change measurement accuracy by considering both image scans simultaneously. Improvements in segmentation are evaluated by measuring volume changes in benign or slow growing nodules. In the analysis of 50 nodules, the variance in percent volume change was reduced from 11.54% to 9.35% (p=0.03) through the use of registration, adaptive thresholding, and knowledge-based shape matching.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Biopsy</subject><subject>Cancer</subject><subject>Computed tomography</subject><subject>Growth rate</subject><subject>growth rate estimation</subject><subject>Humans</subject><subject>Image registration</subject><subject>Image segmentation</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Information Storage and Retrieval - methods</subject><subject>Lungs</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Phantoms, Imaging</subject><subject>pulmonary nodules</subject><subject>Radiographic Image Enhancement - methods</subject><subject>Radiographic Image Interpretation, Computer-Assisted - methods</subject><subject>Radiography</subject><subject>Radiography, Thoracic - instrumentation</subject><subject>Radiography, Thoracic - methods</subject><subject>Reproducibility of Results</subject><subject>rule-based segmentation</subject><subject>Sensitivity and Specificity</subject><subject>Severity of Illness Index</subject><subject>Shape</subject><subject>Size measurement</subject><subject>Solitary Pulmonary Nodule - diagnostic imaging</subject><subject>Studies</subject><subject>Subtraction Technique</subject><subject>Tomography, X-Ray Computed - instrumentation</subject><subject>Tomography, X-Ray Computed - methods</subject><subject>Volume measurement</subject><issn>0278-0062</issn><issn>1558-254X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqFkU1LHEEQhhtRshvN2YMgg4fkNGtVT38eRUwiKF4Ucmt6emt2Z5mPdXrnoL8-LbMQ8RAPRR3qqSp4H8ZOERaIYC8f728XHEAtjEYpzAGbo5Qm51L8OWRz4Nrkacpn7GuMGwAUEuwXNkOlwIC0cyYeuqwlH8eh7lbZbk1ZWPtuRVndZbF-payvsu3YtH3nh5es65djQ_GEHVW-ifRt34_Z08-bx-vf-d3Dr9vrq7s8SOC73FtVKiy5L6BUtkSzlKS9CVYbkraqyFo0ogxLCAoq70VR8CoogVqpwgQojtmP6e526J9HijvX1jFQ0_iO-jE6owUa4CgS-f2_pNJGpsJPQZ5iUcqYT0G0Qojp9cUHcNOPQ5dycUZJJSVolaDLCQpDH-NAldsOdZsidQjuzaRLJt2bSTeZTBvn-7Nj2dLyH79Xl4CzCaiJ6N0YQaf1v4oZnwo</recordid><startdate>20060401</startdate><enddate>20060401</enddate><creator>Reeves, A.P.</creator><creator>Chan, A.B.</creator><creator>Yankelevitz, D.F.</creator><creator>Henschke, C.I.</creator><creator>Kressler, B.</creator><creator>Kostis, W.J.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><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>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>NAPCQ</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>20060401</creationdate><title>On measuring the change in size of pulmonary nodules</title><author>Reeves, A.P. ; Chan, A.B. ; Yankelevitz, D.F. ; Henschke, C.I. ; Kressler, B. ; Kostis, W.J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c502t-a96b61b2a30b69b18d5e7a8c978e59ffe99184bcd0c60faa4332fc64176638c03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Biopsy</topic><topic>Cancer</topic><topic>Computed tomography</topic><topic>Growth rate</topic><topic>growth rate estimation</topic><topic>Humans</topic><topic>Image registration</topic><topic>Image segmentation</topic><topic>Imaging, Three-Dimensional - methods</topic><topic>Information Storage and Retrieval - methods</topic><topic>Lungs</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Phantoms, Imaging</topic><topic>pulmonary nodules</topic><topic>Radiographic Image Enhancement - methods</topic><topic>Radiographic Image Interpretation, Computer-Assisted - methods</topic><topic>Radiography</topic><topic>Radiography, Thoracic - instrumentation</topic><topic>Radiography, Thoracic - methods</topic><topic>Reproducibility of Results</topic><topic>rule-based segmentation</topic><topic>Sensitivity and Specificity</topic><topic>Severity of Illness Index</topic><topic>Shape</topic><topic>Size measurement</topic><topic>Solitary Pulmonary Nodule - diagnostic imaging</topic><topic>Studies</topic><topic>Subtraction Technique</topic><topic>Tomography, X-Ray Computed - instrumentation</topic><topic>Tomography, X-Ray Computed - methods</topic><topic>Volume measurement</topic><toplevel>online_resources</toplevel><creatorcontrib>Reeves, A.P.</creatorcontrib><creatorcontrib>Chan, A.B.</creatorcontrib><creatorcontrib>Yankelevitz, D.F.</creatorcontrib><creatorcontrib>Henschke, C.I.</creatorcontrib><creatorcontrib>Kressler, B.</creatorcontrib><creatorcontrib>Kostis, W.J.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on medical imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Reeves, A.P.</au><au>Chan, A.B.</au><au>Yankelevitz, D.F.</au><au>Henschke, C.I.</au><au>Kressler, B.</au><au>Kostis, W.J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On measuring the change in size of pulmonary nodules</atitle><jtitle>IEEE transactions on medical imaging</jtitle><stitle>TMI</stitle><addtitle>IEEE Trans Med Imaging</addtitle><date>2006-04-01</date><risdate>2006</risdate><volume>25</volume><issue>4</issue><spage>435</spage><epage>450</epage><pages>435-450</pages><issn>0278-0062</issn><eissn>1558-254X</eissn><coden>ITMID4</coden><abstract>The pulmonary nodule is the most common manifestation of lung cancer, the most deadly of all cancers. Most small pulmonary nodules are benign, however, and currently the growth rate of the nodule provides for one of the most accurate noninvasive methods of determining malignancy. In this paper, we present methods for measuring the change in nodule size from two computed tomography image scans recorded at different times; from this size change the growth rate may be established. The impact of partial voxels for small nodules is evaluated and isotropic resampling is shown to improve measurement accuracy. Methods for nodule location and sizing, pleural segmentation, adaptive thresholding, image registration, and knowledge-based shape matching are presented. The latter three techniques provide for a significant improvement in volume change measurement accuracy by considering both image scans simultaneously. Improvements in segmentation are evaluated by measuring volume changes in benign or slow growing nodules. In the analysis of 50 nodules, the variance in percent volume change was reduced from 11.54% to 9.35% (p=0.03) through the use of registration, adaptive thresholding, and knowledge-based shape matching.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>16608059</pmid><doi>10.1109/TMI.2006.871548</doi><tpages>16</tpages></addata></record> |
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| ispartof | IEEE transactions on medical imaging, 2006-04, Vol.25 (4), p.435-450 |
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| subjects | Algorithms Artificial Intelligence Biopsy Cancer Computed tomography Growth rate growth rate estimation Humans Image registration Image segmentation Imaging, Three-Dimensional - methods Information Storage and Retrieval - methods Lungs Pattern Recognition, Automated - methods Phantoms, Imaging pulmonary nodules Radiographic Image Enhancement - methods Radiographic Image Interpretation, Computer-Assisted - methods Radiography Radiography, Thoracic - instrumentation Radiography, Thoracic - methods Reproducibility of Results rule-based segmentation Sensitivity and Specificity Severity of Illness Index Shape Size measurement Solitary Pulmonary Nodule - diagnostic imaging Studies Subtraction Technique Tomography, X-Ray Computed - instrumentation Tomography, X-Ray Computed - methods Volume measurement |
| title | On measuring the change in size of pulmonary nodules |
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