Dual-Stage Deeply Supervised Attention-based Convolutional Neural
Networks for Mandibular Canal Segmentation in CBCT Scans
- URL: http://arxiv.org/abs/2210.03739v1
- Date: Thu, 6 Oct 2022 09:08:56 GMT
- Title: Dual-Stage Deeply Supervised Attention-based Convolutional Neural
Networks for Mandibular Canal Segmentation in CBCT Scans
- Authors: Azka Rehman, Muhammad Usman, Rabeea Jawaid, Shi Sub Byon, Sung Hyun
Kim, Byoung Dai Lee, Byung il Lee and Yeong Gil Shin
- Abstract summary: We propose a novel dual-stage deep learning based scheme for automatic detection of mandibular canal.
Particularly, we first we enhance the CBCT scans by employing the novel histogram-based dynamic windowing scheme.
After enhancement, we design 3D deeply supervised attention U-Net architecture for localize the volume of interest.
- Score: 4.140750794848906
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Accurate segmentation of mandibular canals in lower jaws is important in
dental implantology, in which the implant position and dimensions are currently
determined manually from 3D CT images by medical experts to avoid damaging the
mandibular nerve inside the canal. In this paper, we propose a novel dual-stage
deep learning based scheme for automatic detection of mandibular canal.
Particularly, we first we 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 localize the volume of interest (VOI) which contains the
mandibular canals (i.e., left and right canals). Finally, we employed the
multi-scale input residual U-Net architecture (MS-R-UNet) to accurately segment
the mandibular canals. The proposed method has been rigorously evaluated on 500
scans and results demonstrate that our technique out performs the existing
state-of-the-art methods in term of segmentation performance as well as
robustness.
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