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

Rodrigo Dalvit Carvalho da Silva; Thomas Richard Jenkyn; Victor Alexander Carranza

doi:10.3390/biology10030182

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

Biology (Basel). 2021 Mar 2;10(3):182. doi: 10.3390/biology10030182.

Authors

Rodrigo Dalvit Carvalho da Silva^{1

2}, Thomas Richard Jenkyn^{1

2

3

4

5}, Victor Alexander Carranza^{1

6}

Affiliations

¹ Craniofacial Injury and Concussion Research Laboratory, Western University, London, ON N6A 3K7, Canada.
² School of Biomedical Engineering, Faculty of Engineering, Western University, London, ON N6A 3K7, Canada.
³ Department of Mechanical and Materials Engineering, Western University, London, ON N6A 3K7, Canada.
⁴ School of Kinesiology, Faculty of Health Sciences, Western University, London, ON N6A 3K7, Canada.
⁵ Wolf Orthopaedic Biomechanics Laboratory, Fowler Kennedy Sport Medicine Clinic, London, ON N6A 3K7, Canada.
⁶ School of Biomedical Engineering, Faculty of Engineering, Collaborative Specialization in Musculoskeletal Health Research, and Bone and Joint Institute, Western University, London, ON N6A 3K7, Canada.

Abstract

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.

Keywords: cephalometric analysis; convolutional neural network; craniofacial skeleton; geometric moments.