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20.11.2019 | Original Article

An approach to automated measuring morphological parameters of proximal femora on three-dimensional models

verfasst von: Junlei Hu, Liyu Xu, Mengjie Jing, Henghui Zhang, Liao Wang, Xiaojun Chen

Erschienen in: International Journal of Computer Assisted Radiology and Surgery

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Abstract

Purpose

Analyses of the morphology of proximal femora are essential for preoperative planning and designing customized femoral stems in total hip arthroplasty as well as intramedullary nailing fixation. Various studies reported measurements and analyses on the general geometry of proximal femora three-dimensionally. However, the modeling and measurements are time-consuming and unfriendly to surgeons. Thus, automated measurement and modeling of the femoral medullary canal are critical to promote the clinical application.

Methods

An approach to automated measuring morphological parameters of proximal femur was proposed, and a software allowing importing femur models and manually locating the related anatomic landmarks was developed in the current study. 3D modeling of the femoral medullary canal was created by the semispherical iterative searching algorithm, and 16 key anatomic parameters of the proximal femur were automatically calculated by the least-squares fitting algorithm.

Results

By experimenting on 196 femur STL models, the average execution time of single measurement was 0.88 (SD = 0.13) s, and the intra-class correlation coefficient of 10 anatomic parameters was between 0.880 and 0.996, showing high intra-rater and inter-rater reliability.

Conclusions

The parameters of proximal femur can be easily measured, and the 3D modeling of the femoral medullary canal can be rapidly achieved. The approach will be easily applicable to clinical practice and has the potential to be applied in the design of customized femoral stems.
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Metadaten
Titel
An approach to automated measuring morphological parameters of proximal femora on three-dimensional models
verfasst von
Junlei Hu
Liyu Xu
Mengjie Jing
Henghui Zhang
Liao Wang
Xiaojun Chen
Publikationsdatum
20.11.2019
Verlag
Springer International Publishing
Erschienen in
International Journal of Computer Assisted Radiology and Surgery
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-019-02095-w

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