Development and Validation of Segmentation Method for Lung Cancer Volumetry on Chest CT
The set of criteria called Response Evaluation Criteria In Solid Tumors (RECIST) is used to evaluate the remedial effects of lung cancer, whereby the size of a lesion can be measured in one dimension (diameter). Volumetric evaluation is desirable for estimating the size of a lesion accurately, but t...
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| Veröffentlicht in: | Journal of digital imaging 2018-08, Vol.31 (4), p.505-512 |
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| creator | Kim, Young Jae Lee, Seung Hyun Lim, Kun Young Kim, Kwang Gi |
| description | The set of criteria called Response Evaluation Criteria In Solid Tumors (RECIST) is used to evaluate the remedial effects of lung cancer, whereby the size of a lesion can be measured in one dimension (diameter). Volumetric evaluation is desirable for estimating the size of a lesion accurately, but there are several constraints and limitations to calculating the volume in clinical trials. In this study, we developed a method to detect lesions automatically, with minimal intervention by the user, and calculate their volume. Our proposed method, called a spherical region-growing method (SPRG), uses segmentation that starts from a seed point set by the user. SPRG is a modification of an existing region-growing method that is based on a sphere instead of pixels. The SPRG method detects lesions while preventing leakage to neighboring tissues, because the sphere is grown, i.e., neighboring voxels are added, only when all the voxels meet the required conditions. In this study, two radiologists segmented lung tumors using a manual method and the proposed method, and the results of both methods were compared. The proposed method showed a high sensitivity of 81.68–84.81% and a high dice similarity coefficient (DSC) of 0.86–0.88 compared with the manual method. In addition, the SPRG intraclass correlation coefficient (ICC) was 0.998 (CI 0.997–0.999,
p
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| doi_str_mv | 10.1007/s10278-018-0051-5 |
| format | Article |
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p
< 0.01), showing that the SPRG method is highly reliable. If our proposed method is used for segmentation and volumetric measurement of lesions, then objective and accurate results and shorter data analysis time are possible.</description><identifier>ISSN: 0897-1889</identifier><identifier>ISSN: 1618-727X</identifier><identifier>EISSN: 1618-727X</identifier><identifier>DOI: 10.1007/s10278-018-0051-5</identifier><identifier>PMID: 29380154</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>ANIMAL TISSUES ; CALORIMETRY ; Cancer ; CLINICAL TRIALS ; COMPUTERIZED TOMOGRAPHY ; Correlation coefficient ; Correlation coefficients ; CORRELATIONS ; Criteria ; DATA ANALYSIS ; Data processing ; DIAGNOSIS ; Evaluation ; Imaging ; Lesions ; Lung cancer ; MANUALS ; Mathematical analysis ; Medical diagnosis ; Medical research ; Medicine ; Medicine & Public Health ; NEOPLASMS ; Radiology ; RADIOLOGY AND NUCLEAR MEDICINE ; Segmentation ; SENSITIVITY ; Solid tumors ; SPHERICAL CONFIGURATION ; Tissues ; Tumors</subject><ispartof>Journal of digital imaging, 2018-08, Vol.31 (4), p.505-512</ispartof><rights>Society for Imaging Informatics in Medicine 2018</rights><rights>Journal of Digital Imaging is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c498t-67ab64d6bd740f41100b0aa6806939d4d4d49ea71a21af6ad8ac39503917c5b93</citedby><cites>FETCH-LOGICAL-c498t-67ab64d6bd740f41100b0aa6806939d4d4d49ea71a21af6ad8ac39503917c5b93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6113144/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6113144/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29380154$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/biblio/22795609$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Kim, Young Jae</creatorcontrib><creatorcontrib>Lee, Seung Hyun</creatorcontrib><creatorcontrib>Lim, Kun Young</creatorcontrib><creatorcontrib>Kim, Kwang Gi</creatorcontrib><title>Development and Validation of Segmentation Method for Lung Cancer Volumetry on Chest CT</title><title>Journal of digital imaging</title><addtitle>J Digit Imaging</addtitle><addtitle>J Digit Imaging</addtitle><description>The set of criteria called Response Evaluation Criteria In Solid Tumors (RECIST) is used to evaluate the remedial effects of lung cancer, whereby the size of a lesion can be measured in one dimension (diameter). Volumetric evaluation is desirable for estimating the size of a lesion accurately, but there are several constraints and limitations to calculating the volume in clinical trials. In this study, we developed a method to detect lesions automatically, with minimal intervention by the user, and calculate their volume. Our proposed method, called a spherical region-growing method (SPRG), uses segmentation that starts from a seed point set by the user. SPRG is a modification of an existing region-growing method that is based on a sphere instead of pixels. The SPRG method detects lesions while preventing leakage to neighboring tissues, because the sphere is grown, i.e., neighboring voxels are added, only when all the voxels meet the required conditions. In this study, two radiologists segmented lung tumors using a manual method and the proposed method, and the results of both methods were compared. The proposed method showed a high sensitivity of 81.68–84.81% and a high dice similarity coefficient (DSC) of 0.86–0.88 compared with the manual method. In addition, the SPRG intraclass correlation coefficient (ICC) was 0.998 (CI 0.997–0.999,
p
< 0.01), showing that the SPRG method is highly reliable. If our proposed method is used for segmentation and volumetric measurement of lesions, then objective and accurate results and shorter data analysis time are possible.</description><subject>ANIMAL TISSUES</subject><subject>CALORIMETRY</subject><subject>Cancer</subject><subject>CLINICAL TRIALS</subject><subject>COMPUTERIZED TOMOGRAPHY</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>CORRELATIONS</subject><subject>Criteria</subject><subject>DATA ANALYSIS</subject><subject>Data processing</subject><subject>DIAGNOSIS</subject><subject>Evaluation</subject><subject>Imaging</subject><subject>Lesions</subject><subject>Lung cancer</subject><subject>MANUALS</subject><subject>Mathematical analysis</subject><subject>Medical diagnosis</subject><subject>Medical research</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>NEOPLASMS</subject><subject>Radiology</subject><subject>RADIOLOGY AND NUCLEAR MEDICINE</subject><subject>Segmentation</subject><subject>SENSITIVITY</subject><subject>Solid tumors</subject><subject>SPHERICAL CONFIGURATION</subject><subject>Tissues</subject><subject>Tumors</subject><issn>0897-1889</issn><issn>1618-727X</issn><issn>1618-727X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1UU1v1DAQtRCILoUfwAVZ4sIl4En8eUFC4avSVhwohZvlOM5uqsRe7KRS_z0OKdtyqKyRNZo3783MQ-glkLdAiHiXgJRCFgRyEAYFe4Q2wHMmSvHrMdoQqUQBUqoT9CylK0JAMEGfopNSVZIAoxv086O7dkM4jM5P2PgWX5qhb83UB49Dh7-73VJZ83M37UOLuxDxdvY7XBtvXcSXYZhHN8UbnDH13qUJ1xfP0ZPODMm9uP1P0Y_Pny7qr8X225ez-sO2sFTJqeDCNJy2vGkFJR2FvFZDjOGScFWpli5POSPAlGA6blppbKUYqRQIyxpVnaL3K-9hbkbX2jxsNIM-xH408UYH0-v_K77f61241hygAkozweuVIKSp18n2k7N7G7x3dtJlKRTjZJF5cysTw-85r6jHPlk3DMa7MCcNSlUE8kmrO8Ij9CrM0ecj_EWxEmS5EMKKsjGkFF13HBmIXszVq7k6m6sXczXLPa_u73rs-OdmBpQrIOWS37l4T_pB1j87866R</recordid><startdate>20180801</startdate><enddate>20180801</enddate><creator>Kim, Young Jae</creator><creator>Lee, Seung Hyun</creator><creator>Lim, Kun Young</creator><creator>Kim, 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and Validation of Segmentation Method for Lung Cancer Volumetry on Chest CT</title><author>Kim, Young Jae ; Lee, Seung Hyun ; Lim, Kun Young ; Kim, Kwang Gi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c498t-67ab64d6bd740f41100b0aa6806939d4d4d49ea71a21af6ad8ac39503917c5b93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>ANIMAL TISSUES</topic><topic>CALORIMETRY</topic><topic>Cancer</topic><topic>CLINICAL TRIALS</topic><topic>COMPUTERIZED TOMOGRAPHY</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>CORRELATIONS</topic><topic>Criteria</topic><topic>DATA ANALYSIS</topic><topic>Data processing</topic><topic>DIAGNOSIS</topic><topic>Evaluation</topic><topic>Imaging</topic><topic>Lesions</topic><topic>Lung cancer</topic><topic>MANUALS</topic><topic>Mathematical analysis</topic><topic>Medical diagnosis</topic><topic>Medical research</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>NEOPLASMS</topic><topic>Radiology</topic><topic>RADIOLOGY AND NUCLEAR MEDICINE</topic><topic>Segmentation</topic><topic>SENSITIVITY</topic><topic>Solid tumors</topic><topic>SPHERICAL CONFIGURATION</topic><topic>Tissues</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Young Jae</creatorcontrib><creatorcontrib>Lee, Seung Hyun</creatorcontrib><creatorcontrib>Lim, Kun Young</creatorcontrib><creatorcontrib>Kim, Kwang Gi</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Computer and Information Systems Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical 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Gi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development and Validation of Segmentation Method for Lung Cancer Volumetry on Chest CT</atitle><jtitle>Journal of digital imaging</jtitle><stitle>J Digit Imaging</stitle><addtitle>J Digit Imaging</addtitle><date>2018-08-01</date><risdate>2018</risdate><volume>31</volume><issue>4</issue><spage>505</spage><epage>512</epage><pages>505-512</pages><issn>0897-1889</issn><issn>1618-727X</issn><eissn>1618-727X</eissn><abstract>The set of criteria called Response Evaluation Criteria In Solid Tumors (RECIST) is used to evaluate the remedial effects of lung cancer, whereby the size of a lesion can be measured in one dimension (diameter). Volumetric evaluation is desirable for estimating the size of a lesion accurately, but there are several constraints and limitations to calculating the volume in clinical trials. In this study, we developed a method to detect lesions automatically, with minimal intervention by the user, and calculate their volume. Our proposed method, called a spherical region-growing method (SPRG), uses segmentation that starts from a seed point set by the user. SPRG is a modification of an existing region-growing method that is based on a sphere instead of pixels. The SPRG method detects lesions while preventing leakage to neighboring tissues, because the sphere is grown, i.e., neighboring voxels are added, only when all the voxels meet the required conditions. In this study, two radiologists segmented lung tumors using a manual method and the proposed method, and the results of both methods were compared. The proposed method showed a high sensitivity of 81.68–84.81% and a high dice similarity coefficient (DSC) of 0.86–0.88 compared with the manual method. In addition, the SPRG intraclass correlation coefficient (ICC) was 0.998 (CI 0.997–0.999,
p
< 0.01), showing that the SPRG method is highly reliable. If our proposed method is used for segmentation and volumetric measurement of lesions, then objective and accurate results and shorter data analysis time are possible.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>29380154</pmid><doi>10.1007/s10278-018-0051-5</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | ANIMAL TISSUES CALORIMETRY Cancer CLINICAL TRIALS COMPUTERIZED TOMOGRAPHY Correlation coefficient Correlation coefficients CORRELATIONS Criteria DATA ANALYSIS Data processing DIAGNOSIS Evaluation Imaging Lesions Lung cancer MANUALS Mathematical analysis Medical diagnosis Medical research Medicine Medicine & Public Health NEOPLASMS Radiology RADIOLOGY AND NUCLEAR MEDICINE Segmentation SENSITIVITY Solid tumors SPHERICAL CONFIGURATION Tissues Tumors |
| title | Development and Validation of Segmentation Method for Lung Cancer Volumetry on Chest CT |
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