| Home > Articles > Integration of cortical thickness data in a statistical shape model of the scapula > MARC |
000088567 001__ 88567 000088567 005__ 20220405150415.0 000088567 0247_ $$2doi$$a10.1080/10255842.2020.1757082 000088567 0248_ $$2sideral$$a117362 000088567 037__ $$aART-2020-117362 000088567 041__ $$aeng 000088567 100__ $$aPitocchi, Jonathan 000088567 245__ $$aIntegration of cortical thickness data in a statistical shape model of the scapula 000088567 260__ $$c2020 000088567 5060_ $$aAccess copy available to the general public$$fUnrestricted 000088567 5203_ $$aKnowledge about bone morphology and bone quality of the scapula throughout the population is fundamental in the design of shoulder implants. In particular, regions with the best bone stock (cortical bone) are taken into account when planning the supporting screws, aiming for an optimal fixation. As an alternative to manual measurements, statistical shape models (SSMs) have been commonly used to describe shape variability within a population. However, explicitly including cortical thickness information in an SSM of the scapula still remains a challenge. Therefore, the goal of this study is to combine scapular bone shape and cortex morphology in an SSM. First, a method to estimate cortical thickness, based on HU (Hounsfield Unit) profile analysis, was developed and validated. Then, based on the manual segmentations of 32 healthy scapulae, a statistical shape model including cortical information was created and evaluated. Generalization, specificity and compactness were calculated in order to assess the quality of the SSM. The average cortical thickness of the SSM was 2.0¿±¿0.63¿mm. Generalization, specificity and compactness performances confirmed that the combined SSM was able to capture the bone quality changes in the population. In this work we integrated information on the cortical thickness in an SSM for the scapula. From the results we conclude that this methodology is a valuable tool for automatically generating a large population of scapulae and deducing statistics on the cortex. Hence, this SSM can be useful to automate implant design and screw placement in shoulder arthroplasty. 000088567 536__ $$9info:eu-repo/grantAgreement/EC/H2020/722535/EU/Predictive models and simulations in bone regeneration: a multiscale patient-specific approach/CuraBone$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 722535-CuraBone 000088567 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000088567 590__ $$a1.763$$b2020 000088567 591__ $$aENGINEERING, BIOMEDICAL$$b72 / 90 = 0.8$$c2020$$dQ4$$eT3 000088567 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b86 / 112 = 0.768$$c2020$$dQ4$$eT3 000088567 592__ $$a0.353$$b2020 000088567 593__ $$aBioengineering$$c2020$$dQ3 000088567 593__ $$aBiomedical Engineering$$c2020$$dQ3 000088567 593__ $$aMedicine (miscellaneous)$$c2020$$dQ3 000088567 593__ $$aHuman-Computer Interaction$$c2020$$dQ3 000088567 593__ $$aComputer Science Applications$$c2020$$dQ3 000088567 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000088567 700__ $$aWirix-Speetjens, Roel 000088567 700__ $$avan Lenthe, G. Harry 000088567 700__ $$0(orcid)0000-0002-2901-4188$$aPérez, María Ángeles$$uUniversidad de Zaragoza 000088567 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est. 000088567 773__ $$g23, 10 (2020), 642-648$$pComput. methods biomech. biomed. eng.$$tComputer Methods in Biomechanics and Biomedical Engineering$$x1025-5842 000088567 8564_ $$s783443$$uhttps://zaguan.unizar.es/record/88567/files/texto_completo.pdf$$yVersión publicada 000088567 8564_ $$s45051$$uhttps://zaguan.unizar.es/record/88567/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000088567 909CO $$ooai:zaguan.unizar.es:88567$$particulos$$pdriver 000088567 951__ $$a2022-04-05-14:36:45 000088567 980__ $$aARTICLE
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