Automatic tracing of mandibular canal pathways using deep learning

TL;DR

This paper presents an automated deep learning framework using 3D U-Net to accurately trace mandibular canal pathways in CBCT scans, improving efficiency and precision over manual methods.

Contribution

A novel end-to-end 3D deep learning approach that generates centerline ground truths and includes an effective post-processing step for mandibular canal detection.

Findings

High segmentation accuracy demonstrated by F1-score and IoU metrics

Effective distance-based measurements with low mean curve distance

Robust performance across extensive experiments

Abstract

There is an increasing demand in medical industries to have automated systems for detection and localization which are manually inefficient otherwise. In dentistry, it bears great interest to trace the pathway of mandibular canals accurately. Proper localization of the position of the mandibular canals, which surrounds the inferior alveolar nerve (IAN), reduces the risk of damaging it during dental implantology. Manual detection of canal paths is not an efficient way in terms of time and labor. Here, we propose a deep learning-based framework to detect mandibular canals from CBCT data. It is a 3-stage process fully automatic end-to-end. Ground truths are generated in the preprocessing stage. Instead of using commonly used fixed diameter tubular-shaped ground truth, we generate centerlines of the mandibular canals and used them as ground truths in the training process. A 3D U-Net architecture is used for model training. An efficient post-processing stage is developed to rectify the initial prediction. The precision, recall, F1-score, and IoU are measured to analyze the voxel-level segmentation performance. However, to analyze the distance-based measurements, mean curve distance (MCD) both from ground truth to prediction and prediction to ground truth is calculated. Extensive experiments are conducted to demonstrate the effectiveness of the model.