Automated Segmentation of the Lumbar Pedicle in CT Images for Spinal Fusion Surgery
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Jongwon | - |
dc.contributor.author | Kim, Sungmin | - |
dc.contributor.author | Kim, Young Soo | - |
dc.contributor.author | Chung, Wan Kyun | - |
dc.date.accessioned | 2022-07-16T19:56:27Z | - |
dc.date.available | 2022-07-16T19:56:27Z | - |
dc.date.created | 2021-05-12 | - |
dc.date.issued | 2011-07 | - |
dc.identifier.issn | 0018-9294 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/168056 | - |
dc.description.abstract | Exact information about the shape of a lumbar pedicle can increase operation accuracy and safety during computer-aided spinal fusion surgery, which requires extreme caution on the part of the surgeon, due to the complexity and delicacy of the procedure. In this paper, a robust framework for segmenting the lumbar pedicle in computed tomography (CT) images is presented. The framework that has been designed takes a CT image, which includes the lumbar pedicle as input, and provides the segmented lumbar pedicle in the form of 3-D voxel sets. This multistep approach begins with 2-D dynamic thresholding using local optimal thresholds, followed by procedures to recover the spine geometry in a high curvature environment. A subsequent canal reference determination using proposed thinning-based integrated cost is then performed. Based on the obtained segmented vertebra and canal reference, the edge of the spinal pedicle is segmented. This framework has been tested on 84 lumbar vertebrae of 19 patients requiring spinal fusion. It was successfully applied, resulting in an average success rate of 93.22% and a final mean error of 0.14 +/- 0.05 mm. Precision errors were smaller than 1% for spine pedicle volumes. Intra-and interoperator precision errors were not significantly different. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Automated Segmentation of the Lumbar Pedicle in CT Images for Spinal Fusion Surgery | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Young Soo | - |
dc.identifier.doi | 10.1109/TBME.2011.2135351 | - |
dc.identifier.scopusid | 2-s2.0-79959535980 | - |
dc.identifier.wosid | 000291890000020 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, v.58, no.7, pp.2051 - 2063 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | - |
dc.citation.title | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | - |
dc.citation.volume | 58 | - |
dc.citation.number | 7 | - |
dc.citation.startPage | 2051 | - |
dc.citation.endPage | 2063 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Biomedical | - |
dc.subject.keywordPlus | SCREW PLACEMENT | - |
dc.subject.keywordPlus | REGION | - |
dc.subject.keywordPlus | ACCURACY | - |
dc.subject.keywordPlus | DEFINITION | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.subject.keywordPlus | CORD | - |
dc.subject.keywordAuthor | Computed tomography | - |
dc.subject.keywordAuthor | geometry recovery | - |
dc.subject.keywordAuthor | lumbar spine | - |
dc.subject.keywordAuthor | spine surgery | - |
dc.subject.keywordAuthor | 3-D pedicle segmentation | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/5740956 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.