English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Accurate segmentation of mandibular canals in lower jaws is important indental implantology. Medical experts determine the implant position anddimensions manually from 3D CT images to avoid damaging the mandibular nerveinside the canal. In this paper, we propose a novel dual-stage deeplearning-based scheme for the automatic segmentation of the mandibular canal.Particularly, we first enhance the CBCT scans by employing the novelhistogram-based dynamic windowing scheme, which improves the visibility ofmandibular canals. After enhancement, we design 3D deeply supervised attentionU-Net architecture for localizing the volumes of interest (VOIs), which containthe mandibular canals (i.e., left and right canals). Finally, we employed themulti-scale input residual U-Net architecture (MS-R-UNet) to segment themandibular canals using VOIs accurately. The proposed method has beenrigorously evaluated on 500 scans. The results demonstrate that our techniqueoutperforms the current state-of-the-art segmentation performance androbustness methods.

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