Automated Vertebral Segmentation and Measurement of Vertebral Compression Ratio Based on Deep Learning in X-Ray Images
DC Field | Value | Language |
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dc.contributor.author | Kim, Dong Hyun | - |
dc.contributor.author | Jeong, Jin Gyo | - |
dc.contributor.author | Kim, Young Jae | - |
dc.contributor.author | Kim, Kwang Gi | - |
dc.contributor.author | Jeon, Ji Young | - |
dc.date.accessioned | 2021-09-30T00:40:46Z | - |
dc.date.available | 2021-09-30T00:40:46Z | - |
dc.date.created | 2021-07-19 | - |
dc.date.issued | 2021-08 | - |
dc.identifier.issn | 0897-1889 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/82246 | - |
dc.description.abstract | Vertebral compression fracture is a deformity of vertebral bodies found on lateral spine images. To diagnose vertebral compression fracture, accurate measurement of vertebral compression ratio is required. Therefore, rapid and accurate segmentation of vertebra is important for measuring the vertebral compression ratio. In this study, we used 339 data of lateral thoracic and lumbar vertebra images for training and testing a deep learning model for segmentation. The result of segmentation by the model was compared with the manual measurement, which is performed by a specialist. As a result, the average sensitivity of the dataset was 0.937, specificity was 0.995, accuracy was 0.992, and dice similarity coefficient was 0.929, area under the curve of receiver operating characteristic curve was 0.987, and the precision recall curve was 0.916. The result of correlation analysis shows no statistical difference between the manually measured vertebral compression ratio and the vertebral compression ratio using the data segmented by the model in which the correlation coefficient was 0.929. In addition, the Bland-Altman plot shows good equivalence in which VCR values are in the area within average +/- 1.96. In conclusion, vertebra segmentation based on deep learning is expected to be helpful for the measurement of vertebral compression ratio. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.relation.isPartOf | JOURNAL OF DIGITAL IMAGING | - |
dc.title | Automated Vertebral Segmentation and Measurement of Vertebral Compression Ratio Based on Deep Learning in X-Ray Images | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000670856800003 | - |
dc.identifier.doi | 10.1007/s10278-021-00471-0 | - |
dc.identifier.bibliographicCitation | JOURNAL OF DIGITAL IMAGING, v.34, no.4, pp.853 - 861 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85109969967 | - |
dc.citation.endPage | 861 | - |
dc.citation.startPage | 853 | - |
dc.citation.title | JOURNAL OF DIGITAL IMAGING | - |
dc.citation.volume | 34 | - |
dc.citation.number | 4 | - |
dc.contributor.affiliatedAuthor | Kim, Dong Hyun | - |
dc.contributor.affiliatedAuthor | Jeong, Jin Gyo | - |
dc.contributor.affiliatedAuthor | Kim, Young Jae | - |
dc.contributor.affiliatedAuthor | Kim, Kwang Gi | - |
dc.contributor.affiliatedAuthor | Jeon, Ji Young | - |
dc.type.docType | Article; Early Access | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Segmentation | - |
dc.subject.keywordAuthor | Vertebral compression fracture | - |
dc.subject.keywordAuthor | Vertebral compression ratio | - |
dc.subject.keywordPlus | HEIGHT LOSS | - |
dc.subject.keywordPlus | FRACTURES | - |
dc.subject.keywordPlus | SPINE | - |
dc.subject.keywordPlus | KYPHOSIS | - |
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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