Automatic Cephalometric Landmark Identification System Based on the Multi-Stage Convolutional Neural Networks with CBCT Combination Images.
Sensors (Basel)
; 21(2)2021 Jan 12.
Article
em En
| MEDLINE
| ID: mdl-33445758
ABSTRACT
This study was designed to develop and verify a fully automated cephalometry landmark identification system, based on multi-stage convolutional neural networks (CNNs) architecture, using a combination dataset. In this research, we trained and tested multi-stage CNNs with 430 lateral and 430 MIP lateral cephalograms synthesized by cone-beam computed tomography (CBCT) to make a combination dataset. Fifteen landmarks were manually and respectively identified by experienced examiner, at the preprocessing phase. The intra-examiner reliability was high (ICC = 0.99) in manual identification. The results of prediction of the system for average mean radial error (MRE) and standard deviation (SD) were 1.03 mm and 1.29 mm, respectively. In conclusion, different types of image data might be the one of factors that affect the prediction accuracy of a fully-automated landmark identification system, based on multi-stage CNNs.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Cefalometria
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Redes Neurais de Computação
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Tomografia Computadorizada de Feixe Cônico
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Pontos de Referência Anatômicos
Tipo de estudo:
Diagnostic_studies
/
Observational_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Sensors (Basel)
Ano de publicação:
2021
Tipo de documento:
Article