Mandible Segmentation of Dental CBCT Scans Affected by Metal Artifacts Using Coarse-to-Fine Learning Model

Mandible Segmentation of Dental CBCT Scans Affected by Metal Artifacts Using Coarse-to-Fine Learning Model

Accurate segmentation of the mandible from cone-beam computed tomography (CBCT) scans is an important step for building a personalized 3D digital mandible model for maxillofacial surgery and orthodontic treatment planning because of the low radiation dose and short scanning duration. CBCT images, ho...

Full description

Bibliographic Details
Main Authors: Bingjiang Qiu, Hylke van der Wel, Joep Kraeima, Haye Hendrik Glas, Jiapan Guo, Ronald J. H. Borra, Max Johannes Hendrikus Witjes, Peter M. A. van Ooijen
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Journal of Personalized Medicine
Subjects:
mandible segmentation
cone-beam computed tomography (CBCT)
computed tomography (CT)
metal artifacts
3D virtual surgical planning (3D VSP)
convolutional neural networks
Online Access:https://www.mdpi.com/2075-4426/11/6/560

Internet

https://www.mdpi.com/2075-4426/11/6/560

Similar Items