Dual-Stage Deeply Supervised Attention-based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans

TL;DR

This paper introduces a dual-stage deep learning approach with attention mechanisms and enhanced imaging techniques for accurate mandibular canal segmentation in CBCT scans, improving upon existing methods.

Contribution

The study presents a novel dual-stage deep learning framework combining histogram-based enhancement and attention U-Net architectures for mandibular canal segmentation.

Findings

Outperforms current state-of-the-art methods in accuracy

Demonstrates robustness across 500 CBCT scans

Effective in localizing mandibular canals with high precision

Abstract

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.