  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.
