An Explainable Deep Learning Framework for Mandibular Canal Segmentation from Cone Beam Computed Tomography volumes

Abstract

Cone Beam Computed Tomography (CBCT) is an indispensable imaging modality in oral radiology, offering comprehensive dental anatomical information. Accurate detection of the mandibular canal (MC), a crucial anatomical structure in the lower jaw, within CBCT volumes is essential to support clinical dentistry workflows, including diagnosis, preoperative treatment planning, and postoperative evaluation. In this study, we present a deep learning-based (DL) approach for MC segmentation using 3D U-Net and 3D Attention U-Net networks. We collected a unique dataset of CBCT scans from 20 anonymous hemisected mandibular bones, which were further processed for analysis. The samples were scanned using a CBCT scanner after inserting a wire through the whole length of the MC to identify its location in space (as a gold standard). Our experimental results demonstrate that the 3D Attention U-Net outperforms the standard 3D U-Net in detecting the MC’s location, with Dice similarity score, Precision, and Recall values of 0.65, 0.75, and 0.60, respectively. Unlike current DL-enabled methods for MC segmentation, which face deployment and trust challenges due to their ”black-box” nature, our approach incorporates a post-hoc visual explainability feature through the Grad-CAM++ (Gradient-weighted Class Activation Mapping) algorithm. This tool highlights important regions within the CBCT volumes that influence the model’s predictions, providing valuable insights into the segmentation process, and bridging the gap between cutting-edge DL technology and clinical practice.

Divisions: College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies
College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Aston Centre for Artifical Intelligence Research and Application
Additional Information: This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use [https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms], but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/[DOI here when available].
Event Title: The 12th International Conference on Computational Advances in Bio and Medical Sciences
Event Type: Other
Event Dates: 2023-12-11 - 2023-12-13
Uncontrolled Keywords: Dental Cone Beam Computed Tomography · mandibular canal segmentation · U-Net deep learning model · explainable artificial intelligence · Grad-CAM
Last Modified: 24 May 2024 14:19
Date Deposited: 14 Nov 2023 13:05
Full Text Link:
Related URLs: https://link.sp ... nference/iccabs (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2023-11-08
Accepted Date: 2023-11-08
Authors: Barzas, Konstantinos
Fouad, Shereen (ORCID Profile 0000-0002-4965-7017)
Jasa, Gainer
Landini, Gabriel

Download

[img]

Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 1 January 2050.

License: ["licenses_description_other" not defined]


Export / Share Citation


Statistics

Additional statistics for this record