Deep convolutional neural network-based automated segmentation of the maxillofacial complex from cone-beam computed tomography – A validation study.

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Abstract

The present study investigated the accuracy, consistency, and time-efficiency of a novel deep CNN-based model for the automated maxillofacial bone segmentation from CBCT images.A dataset of 144 scans was acquired from two CBCT devices and randomly divided into three subsets: training set (n= 110), validation set (n= 10) and testing set (n=24). A three-dimensional (3D) U-Net (CNN) model was developed, and the achieved automated segmentation was compared with a manual approach.The average time required for automated segmentation was 39.1 seconds with a 204-fold decrease in time consumption compared to manual segmentation (132.7 minutes). The model is highly accurate for identification of the bony structures of the anatomical region of interest with a dice similarity coefficient (DSC) of 92.6%. Additionally, the fully deterministic nature of the CNN model was able to provide 100% consistency without any variability. The inter-observer consistency for expert-based minor correction of the automated segmentation observed an excellent DSC of 99.7%.The proposed CNN model provided a time-efficient, accurate, and consistent CBCT-based automated segmentation of the maxillofacial complex.Automated segmentation of the maxillofacial complex could act as a potent alternative to the conventional segmentation techniques for improving the efficiency of the digital workflows. This approach could deliver an accurate and ready-to-print three dimensional (3D) models that are essential to patient-specific digital treatment planning for orthodontics, maxillofacial surgery, and implant placement.Copyright © 2022. Published by Elsevier Ltd.