AggregateNet: A deep learning model for automated classification of cervical vertebrae maturation stages.
artificial intelligence
cervical vertebrae maturation stages
deep learning
growth and development
multiple input CNN
Journal
Orthodontics & craniofacial research
ISSN: 1601-6343
Titre abrégé: Orthod Craniofac Res
Pays: England
ID NLM: 101144387
Informations de publication
Date de publication:
28 Feb 2023
28 Feb 2023
Historique:
received:
08
02
2023
accepted:
15
02
2023
pubmed:
2
3
2023
medline:
2
3
2023
entrez:
1
3
2023
Statut:
aheadofprint
Résumé
A study of supervised automated classification of the cervical vertebrae maturation (CVM) stages using deep learning (DL) network is presented. A parallel structured deep convolutional neural network (CNN) with a pre-processing layer that takes X-ray images and the age as the input is proposed. A total of 1018 cephalometric radiographs were labelled and classified according to the CVM stages. The images were separated according to gender for better model-fitting. The images were cropped to extract the cervical vertebrae automatically using an object detector. The resulting images and the age inputs were used to train the proposed DL model: AggregateNet with a set of tunable directional edge enhancers. After the features of the images were extracted, the age input was concatenated to the output feature vector. To have the parallel network not overfit, data augmentation was used. The performance of our CNN model was compared with other DL models, ResNet20, Xception, MobileNetV2 and custom-designed CNN model with the directional filters. The proposed innovative model that uses a parallel structured network preceded with a pre-processing layer of edge enhancement filters achieved a validation accuracy of 82.35% in CVM stage classification on female subjects, 75.0% in CVM stage classification on male subjects, exceeding the accuracy achieved with the other DL models investigated. The effectiveness of the directional filters is reflected in the improved performance attained in the results. If AggregateNet is used without directional filters, the test accuracy decreases to 80.0% on female subjects and to 74.03% on male subjects. AggregateNet together with the tunable directional edge filters is observed to produce higher accuracy than the other models that we investigated in the fully automated determination of the CVM stages.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : American Association of Orthodontists Foundation
Organisme : Brodie Craniofacial Endowment
Informations de copyright
© 2023 The Authors. Orthodontics & Craniofacial Research published by John Wiley & Sons Ltd.
Références
Hägg U, Taranger J. Maturation indicators and the pubertal growth spurt. Am J Orthod. 1982;82(4):299-309.
Hunter CJ. The correlation of facial growth with body height and skeletal maturation at adolescence. Angle Orthod. 1966;36(1):44-54.
Tofani MI. Mandibular growth at puberty. Am J Orthod. 1972;62(2):176-195.
Lewis AB, Garn SM. The relationship between tooth formation and other maturational factors. Angle Orthod. 1960;30(2):70-77.
Hägg U, Taranger J. Skeletal stages of the hand and wrist as indicators of the pubertal growth spurt. Acta Odontol Scand. 1980;38(3):187-200.
Garn SM. Radiographic atlas of skeletal development of the hand and wrist. Am J Hum Genet. 1959;11(3):282.
Baccetti T, Franchi L, McNamara JA. The cervical vertebral maturation (CVM) method for the assessment of optimal treatment timing in dentofacial orthopedics. Semin Orthod. 2005;11(3):119-129.
Fishman LS. Radiographic evaluation of skeletal maturation. A clinically oriented method based on hand-wrist films. Angle Orthod. 1982;52(2):88-112.
Baccetti T, Franchi L, Mcnamara JA. An improved version of the cervical vertebral maturation (CVM) method for the assessment of mandibular growth. Angle Orthod. 2002;72(4):316-323.
Uysal T, Ramoglu SI, Basciftci FA, Sari Z. Chronologic age and skeletal maturation of the cervical vertebrae and hand-wrist: is there a relationship? Am J Orthod Dentofacial Orthop. 2006;130(5):622-628.
Nestman TS, Marshall SD, Qian F, Holton N, Franciscus RG, Southard TE. Cervical vertebrae maturation method morphologic criteria: poor reproducibility. Am J Orthod Dentofacial Orthop. 2011;140(2):182-188.
Rainey BJ, Burnside G, Harrison JE. Reliability of cervical vertebral maturation staging. Am J Orthod Dentofacial Orthop. 2016;150(1):98-104.
Lee J-G, Jun S, Cho Y-W, et al. Deep learning in medical imaging: general overview. Korean J Radiol. 2017;18(4):570-584. doi:10.3348/kjr.2017.18.4.570
Seo H, Hwang JJ, Jeong T, Shin J. Comparison of deep learning models for cervical vertebral maturation stage classification on lateral cephalometric radiographs. J Clin Med. 2021;10(16):3591. doi:10.3390/jcm10163591
Mohammad-Rahimi H, Motamadian SR, Nadimi M, et al. Deep learning for the classification of cervical maturation degree and pubertal growth spurts: a pilot study. Korean J Orthodont. 2022;52(2):112-122. doi:10.4041/kjod.2022.52.2.112
Xie S, Girshick R, Dollar P, Tu Z, He K. Aggregated residual transformations for deep neural networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2017. doi:10.1109/cvpr.2017.634
AAOF legacy collection home page. https://www.aaoflegacycollection.org/aaof_home.html. Accessed February 4, 2023
McNamara JA, Franchi L. The cervical vertebral maturation method: a user's guide. Angle Orthod. 2018;88(2):133-143.
Bagci AM, Ansari R, Reynolds WD. Low-complexity implementation of non-subsampled directional filter banks using polyphase representations and generalized separable processing. 2007 IEEE International Conference on Electro/Information Technology. IEEE; 2007.
Bozkurt A, Suhre A, Cetin AE. Multi-scale directional-filtering-based method for follicular lymphoma grading. Signal Image Video Process. 2014;8(S1):63-70. doi:10.1007/s11760-014-0681-0
Russakovsky O, Deng J, Su H, et al. ImageNet large scale visual recognition challenge. Int J Comput Vis. 2015;115(3):211-252. doi:10.1007/s11263-015-0816-y
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2016.
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C. MobileNetV2: inverted residuals and linear bottlenecks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2018. doi:10.1109/cvpr.2018.00474
Chollet F. Xception: deep learning with depthwise separable convolutions. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2017.
Atici SF, Ansari R, Allareddy V, Suhaym O, Cetin AE, Elnagar MH. Fully automated determination of the cervical vertebrae maturation stages using deep learning with directional filters. PLoS One. 2022;17(7):e0269198. doi:10.1371/journal.pone.0269198
Manoochehri H, Motamedi SA, Mohammad-Djafari A, Makaremi M, Vafaie SA. Attention-guided multi-scale CNN network for cervical vertebral maturation assessment from lateral cephalometric radiography. MaxEnt 2022. MDPI; 2022.
Kim EG, Oh IS, So JE, et al. Estimating cervical vertebral maturation with a lateral cephalogram using the convolutional neural network. J Clin Med. 2021;10(22):5400. doi:10.3390/jcm10225400
Haeffele BD, Vidal R. Global optimality in neural network training. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017; 2017. doi:10.1109/cvpr.2017.467