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Robust and Accurate Mandible Segmentation on Dental CBCT Scans Affected by Metal Artifacts Using a Prior Shape Model.

Authors :
Qiu B
van der Wel H
Kraeima J
Hendrik Glas H
Guo J
Borra RJH
Witjes MJH
van Ooijen PMA
Source :
Journal of personalized medicine [J Pers Med] 2021 May 01; Vol. 11 (5). Date of Electronic Publication: 2021 May 01.
Publication Year :
2021

Abstract

Accurate mandible segmentation is significant in the field of maxillofacial surgery to guide clinical diagnosis and treatment and develop appropriate surgical plans. In particular, cone-beam computed tomography (CBCT) images with metal parts, such as those used in oral and maxillofacial surgery (OMFS), often have susceptibilities when metal artifacts are present such as weak and blurred boundaries caused by a high-attenuation material and a low radiation dose in image acquisition. To overcome this problem, this paper proposes a novel deep learning-based approach (SASeg) for automated mandible segmentation that perceives overall mandible anatomical knowledge. SASeg utilizes a prior shape feature extractor (PSFE) module based on a mean mandible shape, and recurrent connections maintain the continuity structure of the mandible. The effectiveness of the proposed network is substantiated on a dental CBCT dataset from orthodontic treatment containing 59 patients. The experiments show that the proposed SASeg can be easily used to improve the prediction accuracy in a dental CBCT dataset corrupted by metal artifacts. In addition, the experimental results on the PDDCA dataset demonstrate that, compared with the state-of-the-art mandible segmentation models, our proposed SASeg can achieve better segmentation performance.

Details

Language :
English
ISSN :
2075-4426
Volume :
11
Issue :
5
Database :
MEDLINE
Journal :
Journal of personalized medicine
Publication Type :
Academic Journal
Accession number :
34062762
Full Text :
https://doi.org/10.3390/jpm11050364