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...

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Detalles Bibliográficos
Autores principales: Qiu, Bingjiang, Guo, Jiapan, Kraeima, Joep, Glas, Haye Hendrik, Zhang, Weichuan, Borra, Ronald J. H., Witjes, Max Johannes Hendrikus, van Ooijen, Peter M. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8229770/
https://www.ncbi.nlm.nih.gov/pubmed/34072714
http://dx.doi.org/10.3390/jpm11060492

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8229770/
https://www.ncbi.nlm.nih.gov/pubmed/34072714
http://dx.doi.org/10.3390/jpm11060492

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