Recurrent Convolutional Neural Networks for 3D Mandible Segmentation in Computed Tomography

Recurrent Convolutional Neural Networks for 3D Mandible Segmentation in Computed Tomography

Purpose: Classic encoder–decoder-based convolutional neural network (EDCNN) approaches cannot accurately segment detailed anatomical structures of the mandible in computed tomography (CT), for instance, condyles and coronoids of the mandible, which are often affected by noise and metal artifacts. Th...

Full description

Bibliographic Details
Main Authors: Bingjiang Qiu, Jiapan Guo, Joep Kraeima, Haye Hendrik Glas, Weichuan Zhang, Ronald J. H. Borra, Max Johannes Hendrikus Witjes, Peter M. A. van Ooijen
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Journal of Personalized Medicine
Subjects:
accurate mandible segmentation
oral and maxillofacial surgery
3D virtual surgical planning (3D VSP)
convolutional neural network
Online Access:https://www.mdpi.com/2075-4426/11/6/492

Internet

https://www.mdpi.com/2075-4426/11/6/492

Similar Items