Integration of cortical thickness data in a statistical shape model of the scapula

Pitocchi, Jonathan ; Wirix-Speetjens, Roel ; van Lenthe, G. Harry ; Pérez, María Ángeles (Universidad de Zaragoza)

Resumen: Knowledge 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.

Idioma: Inglés

DOI: 10.1080/10255842.2020.1757082

Año: 2020

Publicado en: Computer Methods in Biomechanics and Biomedical Engineering 23, 10 (2020), 642-648
ISSN: 1025-5842

Factor impacto JCR: 1.763 (2020)

Categ. JCR: ENGINEERING, BIOMEDICAL rank: 72 / 90 = 0.8 (2020) - Q4 - T3
Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 86 / 112 = 0.768 (2020) - Q4 - T3

Factor impacto SCIMAGO: 0.353 - Bioengineering (Q3) - Biomedical Engineering (Q3) - Medicine (miscellaneous) (Q3) - Human-Computer Interaction (Q3) - Computer Science Applications (Q3)

Financiación: info:eu-repo/grantAgreement/EC/H2020/722535/EU/Predictive models and simulations in bone regeneration: a multiscale patient-specific approach/CuraBone

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)