Analysis of polynomial behavior of the C3 cervical concavity to bone age estimation using artificial neural networks

Moraes, Deovaldo de; Casati, J. P. B.; Rodrigues, Evandro Luís Linhari

doi:10.1109/brc.2013.6487465

Cited by 1 publication

(1 citation statement)

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“…Baptista used the Naive Bayes classifier [11], and Dzemidzic classified CVM using a decision tree [12] after analyzing the rectangular characteristics and the degree of concaveness in the contours of C2, C3, and C4. Moraes approached the regression problem, which outputs bone age by learning the bottom surface of C3 using a simple multi-layer perceptron [13]. Similar to Gray's study, Kök extracted and classified features, such as the width and length of the cervical body, by marking a landmark on the outer edge of the cervical spine [14].…”

Section: Of 12mentioning

confidence: 99%

Estimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network

Kim

et al. 2021

JCM

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Recently, the estimation of bone maturation using deep learning has been actively conducted. However, many studies have considered hand–wrist radiographs, while a few studies have focused on estimating cervical vertebral maturation (CVM) using lateral cephalograms. This study proposes the use of deep learning models for estimating CVM from lateral cephalograms. As the second, third, and fourth cervical vertebral regions (denoted as C2, C3, and C4, respectively) are considerably smaller than the whole image, we propose a stepwise segmentation-based model that focuses on the C2–C4 regions. We propose three convolutional neural network-based classification models: a one-step model with only CVM classification, a two-step model with region of interest (ROI) detection and CVM classification, and a three-step model with ROI detection, cervical segmentation, and CVM classification. Our dataset contains 600 lateral cephalogram images, comprising six classes with 100 images each. The three-step segmentation-based model produced the best accuracy (62.5%) compared to the models that were not segmentation-based.

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Section: Of 12mentioning

confidence: 99%

Estimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network

Kim

et al. 2021

JCM

View full text Add to dashboard Cite

show abstract

Analysis of polynomial behavior of the C3 cervical concavity to bone age estimation using artificial neural networks

Cited by 1 publication

References 7 publications

Estimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network

Estimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network

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