Bone morphological feature extraction for customized bone plate design.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
02 08 2021
Historique:
received: 20 11 2020
accepted: 19 07 2021
entrez: 3 8 2021
pubmed: 4 8 2021
medline: 5 11 2021
Statut: epublish

Résumé

Fractures are difficult to treat because of individual differences in bone morphology and fracture types. Compared to serialized bone plates, the use of customized plates significantly improves the fracture healing process. However, designing custom plates often requires the extraction of skeletal morphology, which is a complex and time-consuming procedure. This study proposes a method for extracting bone morphological features to facilitate customized plate designs. The customized plate design involves three major steps: extracting the morphological features of the bone, representing the undersurface features of the plate, and constructing the customized plate. Among these steps, constructing the undersurface feature involves integrating a group of bone features with different anatomical morphologies into a semantic feature parameter set of the plate feature. The undersurface feature encapsulates the plate and bone features into a highly cohesive generic feature and then establishes an internal correlation between the plate and bone features. Using the femoral plate as an example, we further examined the validity and feasibility of the proposed method. The experimental results demonstrate that the proposed method improves the convenience of redesign through the intuitive editing of semantic parameters. In addition, the proposed method significantly improves the design efficiency and reduces the required design time.

Identifiants

pubmed: 34341376
doi: 10.1038/s41598-021-94924-9
pii: 10.1038/s41598-021-94924-9
pmc: PMC8329034
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

15617

Informations de copyright

© 2021. The Author(s).

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Auteurs

Lin Wang (L)

School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, 221004, People's Republic of China. wlin_xz@xzhmu.edu.cn.

Kaijin Guo (K)

Department of Orthopedics, Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, People's Republic of China.

Kunjin He (K)

College of Internet of Things Engineering, Hohai University, Changzhou, 213022, People's Republic of China.

Hong Zhu (H)

School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, 221004, People's Republic of China.

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