Development and Validation of Segmentation Method for Lung Cancer Volumetry on Chest CT
DC Field | Value | Language |
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dc.contributor.author | Kim, Young Jae | - |
dc.contributor.author | Lee, Seung Hyun | - |
dc.contributor.author | Lim, kun Young | - |
dc.contributor.author | Kim, Kwang Gi | - |
dc.date.available | 2020-02-27T09:42:48Z | - |
dc.date.created | 2020-02-07 | - |
dc.date.issued | 2018-08 | - |
dc.identifier.issn | 0897-1889 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/3514 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.relation.isPartOf | JOURNAL OF DIGITAL IMAGING | - |
dc.title | Development and Validation of Segmentation Method for Lung Cancer Volumetry on Chest CT | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000442992300014 | - |
dc.identifier.doi | 10.1007/s10278-018-0051-5 | - |
dc.identifier.bibliographicCitation | JOURNAL OF DIGITAL IMAGING, v.31, no.4, pp.505 - 512 | - |
dc.identifier.scopusid | 2-s2.0-85041107591 | - |
dc.citation.endPage | 512 | - |
dc.citation.startPage | 505 | - |
dc.citation.title | JOURNAL OF DIGITAL IMAGING | - |
dc.citation.volume | 31 | - |
dc.citation.number | 4 | - |
dc.contributor.affiliatedAuthor | Kim, Young Jae | - |
dc.contributor.affiliatedAuthor | Kim, Kwang Gi | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Lung cancer | - |
dc.subject.keywordAuthor | Response evaluation criteria in solid tumors | - |
dc.subject.keywordAuthor | Tumor volume | - |
dc.subject.keywordAuthor | Computed tomography | - |
dc.subject.keywordAuthor | Computer-assisted diagnosis | - |
dc.subject.keywordPlus | COMPUTER-AIDED DIAGNOSIS | - |
dc.subject.keywordPlus | TUMORS | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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