Development of Local Software for Automatic Measurement of Geometric Parameters in the Proximal Femur Using a Combination of a Deep Learning Approach and an Active Shape Model on X-ray Images.
Active shape model
Deep learning
Femur bone segmentation
Geometric measurement
Proximal femur
Journal
Journal of imaging informatics in medicine
ISSN: 2948-2933
Titre abrégé: J Imaging Inform Med
Pays: Switzerland
ID NLM: 9918663679206676
Informations de publication
Date de publication:
12 Jan 2024
12 Jan 2024
Historique:
received:
23
08
2023
accepted:
23
10
2023
revised:
16
10
2023
medline:
12
2
2024
pubmed:
12
2
2024
entrez:
12
2
2024
Statut:
aheadofprint
Résumé
Proximal femur geometry is an important risk factor for diagnosing and predicting hip and femur injuries. Hence, the development of an automated approach for measuring these parameters could help physicians with the early identification of hip and femur ailments. This paper presents a technique that combines the active shape model (ASM) and deep learning methodologies. First, the femur boundary is extracted by a deep learning neural network. Then, the femur's anatomical landmarks are fitted to the extracted border using the ASM method. Finally, the geometric parameters of the proximal femur, including femur neck axis length (FNAL), femur head diameter (FHD), femur neck width (FNW), shaft width (SW), neck shaft angle (NSA), and alpha angle (AA), are calculated by measuring the distances and angles between the landmarks. The dataset of hip radiographic images consisted of 428 images, with 208 men and 220 women. These images were split into training and testing sets for analysis. The deep learning network and ASM were subsequently trained on the training dataset. In the testing dataset, the automatic measurement of FNAL, FHD, FNW, SW, NSA, and AA parameters resulted in mean errors of 1.19%, 1.46%, 2.28%, 2.43%, 1.95%, and 4.53%, respectively.
Identifiants
pubmed: 38343246
doi: 10.1007/s10278-023-00953-3
pii: 10.1007/s10278-023-00953-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.
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