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Updated: Aug 14, 2025

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural

Tobias Pankert¹, Hyun Lee², Florian Peters²

¹Department of Oral and Maxillofacial Surgery, RWTH Aachen University Hospital, Aachen, Germany. tpankert@ukaachen.de.

International Journal of Computer Assisted Radiology and Surgery

|January 13, 2023

Summary

This study introduces a novel method using cascaded 3D-U-Nets to create accurate, artifact-free 3D mandible models from CT scans, improving surgical planning.

Keywords:

3D-Unet Anatomical curvature Artifact-free segmentation Automated surgical planning CT segmentation Data augmentation Mandible segmentation Medical image segmentation

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A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

Published on: January 28, 2020

Area of Science:

Medical Imaging
Computer-Aided Surgery
Artificial Intelligence

Background:

Segmentation of bone structures from CT data is crucial for computer-aided facial surgery planning.
Metal dental artifacts significantly degrade CT image quality, complicating accurate 3D model creation.
Manual segmentation is labor-intensive and prone to variability.

Purpose of the Study:

To develop an automated method for segmenting accurate, artifact-free 3D surface models of mandibles from CT data.
To overcome limitations posed by metal dental inlays and implants causing imaging artifacts.
To leverage convolutional neural networks for enhanced mandible segmentation.

Main Methods:

A cascaded approach using two independently trained 3D-U-Nets was employed for mandible segmentation.
Networks were trained with varying loss functions and data augmentation techniques.
A dataset of 307 individuals, including cases with severe artifacts, was used for training and evaluation.

Main Results:

The proposed method successfully generated high-resolution 3D mandible segmentations, effectively handling severe CT imaging artifacts.
The two-stepped U-Net approach demonstrated significant improvements in prediction accuracy.
The best models achieved a Dice coefficient of 94.82% and an average surface distance of 0.31 mm.

Conclusions:

Cascaded 3D-U-Nets enable high-resolution predictions for specific regions of interest in medical imaging.
The method offers fast, user-independent, and repeatable image segmentation for automated surgical planning.
This approach provides objective results suitable for integration into computer-aided surgical workflows.