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Clinical Evaluation of Commercial Atlas-Based Auto-Segmentation in the Head and Neck Region

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dc.contributor.author김나리-
dc.contributor.author김주호-
dc.contributor.author김진성-
dc.contributor.author박광우-
dc.contributor.author이응만-
dc.contributor.author이호-
dc.contributor.author장지석-
dc.date.accessioned2019-05-29T05:10:56Z-
dc.date.available2019-05-29T05:10:56Z-
dc.date.issued2019-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/169448-
dc.description.abstractBackground: While atlas segmentation (AS) has proven to be a time-saving and promising method for radiation therapy contouring, optimal methods for its use have not been well-established. Therefore, we investigated the relationship between the size of the atlas patient population and the atlas segmentation auto contouring (AC) performance. Methods: A total of 110 patients' head planning CT images were selected. The mandible and thyroid were selected for this study. The mandibles and thyroids of the patient population were carefully segmented by two skilled clinicians. Of the 110 patients, 100 random patients were registered to 5 different atlas libraries as atlas patients, in groups of 20 to 100, with increments of 20. AS was conducted for each of the remaining 10 patients, either by simultaneous atlas segmentation (SAS) or independent atlas segmentation (IAS). The AS duration of each target patient was recorded. To validate the accuracy of the generated contours, auto contours were compared to manually generated contours (MC) using a volume-overlap-dependent metric, Dice Similarity Coefficient (DSC), and a distance-dependent metric, Hausdorff Distance (HD). Results: In both organs, as the population increased from n = 20 to n = 60, the results showed better convergence. Generally, independent cases produced better performance than simultaneous cases. For the mandible, the best performance was achieved by n = 60 [DSC = 0.92 (0.01) and HD = 6.73 (1.31) mm] and the worst by n = 100 [DSC = 0.90 (0.03) and HD = 10.10 (6.52) mm] atlas libraries. Similar results were achieved with the thyroid; the best performance was achieved by n = 60 [DSC = 0.79 (0.06) and HD = 10.17 (2.89) mm] and the worst by n = 100 [DSC = 0.72 (0.13) and HD = 12.88 (3.94) mm] atlas libraries. Both IAS and SAS showed similar results. Manual contouring of the mandible and thyroid required an average of 1,044 (±170.15) seconds, while AS required an average of 46.4 (±2.8) seconds. Conclusions: The performance of AS AC generally increased as the population of the atlas library increased. However, the performance does not drastically vary in the larger atlas libraries in contrast to the logic that bigger atlas library should lead to better results. In fact, the results do not vary significantly toward the larger atlas library. It is necessary for the institutions to independently research the optimal number of subjects.-
dc.description.statementOfResponsibilityopen-
dc.formatapplication/pdf-
dc.languageEnglish-
dc.publisherFrontiers Research Foundation-
dc.relation.isPartOfFRONTIERS IN ONCOLOGY-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.titleClinical Evaluation of Commercial Atlas-Based Auto-Segmentation in the Head and Neck Region-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Radiation Oncology (방사선종양학교실)-
dc.contributor.googleauthorHyothaek Lee-
dc.contributor.googleauthorEungman Lee-
dc.contributor.googleauthorNalee Kim-
dc.contributor.googleauthorJoo ho Kim-
dc.contributor.googleauthorKwangwoo Park-
dc.contributor.googleauthorHo Lee-
dc.contributor.googleauthorJaehee Chun-
dc.contributor.googleauthorJae-ik Shin-
dc.contributor.googleauthorJee Suk Chang-
dc.contributor.googleauthorJin Sung Kim-
dc.identifier.doi10.3389/fonc.2019.00239-
dc.contributor.localIdA05709-
dc.contributor.localIdA05464-
dc.contributor.localIdA04548-
dc.contributor.localIdA01432-
dc.contributor.localIdA05009-
dc.contributor.localIdA03323-
dc.contributor.localIdA04658-
dc.relation.journalcodeJ03512-
dc.identifier.eissn2234-943X-
dc.identifier.pmid31024843-
dc.subject.keywordatlas segmentation-
dc.subject.keywordatlas-based auto-segmentation-
dc.subject.keywordauto-contouring-
dc.subject.keywordcontouring-
dc.subject.keywordsegmentation-
dc.contributor.alternativeNameKim, Nalee-
dc.contributor.affiliatedAuthor김나리-
dc.contributor.affiliatedAuthor김주호-
dc.contributor.affiliatedAuthor김진성-
dc.contributor.affiliatedAuthor박광우-
dc.contributor.affiliatedAuthor이응만-
dc.contributor.affiliatedAuthor이호-
dc.contributor.affiliatedAuthor장지석-
dc.citation.volume9-
dc.citation.startPage239-
dc.identifier.bibliographicCitationFRONTIERS IN ONCOLOGY, Vol.9 : 239, 2019-
dc.identifier.rimsid62217-
dc.type.rimsART-
Appears in Collections:
6. Others (기타) > Severance Hospital (세브란스병원) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiation Oncology (방사선종양학교실) > 1. Journal Papers

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