  Accurate segmentation of mandibular canals in lower jaws is important in
dental implantology. Medical experts determine the implant position and
dimensions manually from 3D CT images to avoid damaging the mandibular nerve
inside the canal. In this paper, we propose a novel dual-stage deep
learning-based scheme for the automatic segmentation of the mandibular canal.
Particularly, we first enhance the CBCT scans by employing the novel
histogram-based dynamic windowing scheme, which improves the visibility of
mandibular canals. After enhancement, we design 3D deeply supervised attention
U-Net architecture for localizing the volumes of interest (VOIs), which contain
the mandibular canals (i.e., left and right canals). Finally, we employed the
multi-scale input residual U-Net architecture (MS-R-UNet) to segment the
mandibular canals using VOIs accurately. The proposed method has been
rigorously evaluated on 500 scans. The results demonstrate that our technique
outperforms the current state-of-the-art segmentation performance and
robustness methods.
