Convolutional Neural Networks and Geometric Moments to Identify the Bilateral Symmetric Midplane in Facial Skeletons from CT Scans

In reconstructive craniofacial surgery, the bilateral symmetry of the midplane of the facial skeleton plays an important role in surgical planning. Surgeons can take advantage of the intact side of the face as a template for the malformed side by accurately locating the midplane to assist in the pre...

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Main Authors:	Rodrigo Dalvit Carvalho da Silva, Thomas Richard Jenkyn, Victor Alexander Carranza
Format:	Article
Language:	English
Published:	MDPI AG 2021-03-01
Series:	Biology
Subjects:	craniofacial skeleton cephalometric analysis convolutional neural network geometric moments
Online Access:	https://www.mdpi.com/2079-7737/10/3/182

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spelling	doaj-bae707c834254fc9bda787ba0eae7f9b2021-03-03T00:03:57ZengMDPI AGBiology2079-77372021-03-011018218210.3390/biology10030182Convolutional Neural Networks and Geometric Moments to Identify the Bilateral Symmetric Midplane in Facial Skeletons from CT ScansRodrigo Dalvit Carvalho da Silva0Thomas Richard Jenkyn1Victor Alexander Carranza2Craniofacial Injury and Concussion Research Laboratory, Western University, London, ON N6A 3K7, CanadaCraniofacial Injury and Concussion Research Laboratory, Western University, London, ON N6A 3K7, CanadaCraniofacial Injury and Concussion Research Laboratory, Western University, London, ON N6A 3K7, CanadaIn reconstructive craniofacial surgery, the bilateral symmetry of the midplane of the facial skeleton plays an important role in surgical planning. Surgeons can take advantage of the intact side of the face as a template for the malformed side by accurately locating the midplane to assist in the preparation of the surgical procedure. However, despite its importance, the location of the midline is still a subjective procedure. The aim of this study was to present a 3D technique using a convolutional neural network and geometric moments to automatically calculate the craniofacial midline symmetry of the facial skeleton from CT scans. To perform this task, a total of 195 skull images were assessed to validate the proposed technique. In the symmetry planes, the technique was found to be reliable and provided good accuracy. However, further investigations to improve the results of asymmetric images may be carried out.https://www.mdpi.com/2079-7737/10/3/182craniofacial skeletoncephalometric analysisconvolutional neural networkgeometric moments
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Rodrigo Dalvit Carvalho da Silva Thomas Richard Jenkyn Victor Alexander Carranza
spellingShingle	Rodrigo Dalvit Carvalho da Silva Thomas Richard Jenkyn Victor Alexander Carranza Convolutional Neural Networks and Geometric Moments to Identify the Bilateral Symmetric Midplane in Facial Skeletons from CT Scans Biology craniofacial skeleton cephalometric analysis convolutional neural network geometric moments
author_facet	Rodrigo Dalvit Carvalho da Silva Thomas Richard Jenkyn Victor Alexander Carranza
author_sort	Rodrigo Dalvit Carvalho da Silva
title	Convolutional Neural Networks and Geometric Moments to Identify the Bilateral Symmetric Midplane in Facial Skeletons from CT Scans
title_short	Convolutional Neural Networks and Geometric Moments to Identify the Bilateral Symmetric Midplane in Facial Skeletons from CT Scans
title_full	Convolutional Neural Networks and Geometric Moments to Identify the Bilateral Symmetric Midplane in Facial Skeletons from CT Scans
title_fullStr	Convolutional Neural Networks and Geometric Moments to Identify the Bilateral Symmetric Midplane in Facial Skeletons from CT Scans
title_full_unstemmed	Convolutional Neural Networks and Geometric Moments to Identify the Bilateral Symmetric Midplane in Facial Skeletons from CT Scans
title_sort	convolutional neural networks and geometric moments to identify the bilateral symmetric midplane in facial skeletons from ct scans
publisher	MDPI AG
series	Biology
issn	2079-7737
publishDate	2021-03-01
description	In reconstructive craniofacial surgery, the bilateral symmetry of the midplane of the facial skeleton plays an important role in surgical planning. Surgeons can take advantage of the intact side of the face as a template for the malformed side by accurately locating the midplane to assist in the preparation of the surgical procedure. However, despite its importance, the location of the midline is still a subjective procedure. The aim of this study was to present a 3D technique using a convolutional neural network and geometric moments to automatically calculate the craniofacial midline symmetry of the facial skeleton from CT scans. To perform this task, a total of 195 skull images were assessed to validate the proposed technique. In the symmetry planes, the technique was found to be reliable and provided good accuracy. However, further investigations to improve the results of asymmetric images may be carried out.
topic	craniofacial skeleton cephalometric analysis convolutional neural network geometric moments
url	https://www.mdpi.com/2079-7737/10/3/182
work_keys_str_mv	AT rodrigodalvitcarvalhodasilva convolutionalneuralnetworksandgeometricmomentstoidentifythebilateralsymmetricmidplaneinfacialskeletonsfromctscans AT thomasrichardjenkyn convolutionalneuralnetworksandgeometricmomentstoidentifythebilateralsymmetricmidplaneinfacialskeletonsfromctscans AT victoralexandercarranza convolutionalneuralnetworksandgeometricmomentstoidentifythebilateralsymmetricmidplaneinfacialskeletonsfromctscans
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Convolutional Neural Networks and Geometric Moments to Identify the Bilateral Symmetric Midplane in Facial Skeletons from CT Scans

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