Segmentation of dental cone‐beam CT scans affected by metal artifacts using a mixed‐scale dense convolutional neural network

PURPOSE: In order to attain anatomical models, surgical guides and implants for computer‐assisted surgery, accurate segmentation of bony structures in cone‐beam computed tomography (CBCT) scans is required. However, this image segmentation step is often impeded by metal artifacts. Therefore, this st...

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Detalles Bibliográficos
Autores principales: Minnema, Jordi, van Eijnatten, Maureen, Hendriksen, Allard A., Liberton, Niels, Pelt, Daniël M., Batenburg, Kees Joost, Forouzanfar, Tymour, Wolff, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
QUANTITATIVE IMAGING AND IMAGE PROCESSING
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6900023/
https://www.ncbi.nlm.nih.gov/pubmed/31463937
http://dx.doi.org/10.1002/mp.13793

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6900023/
https://www.ncbi.nlm.nih.gov/pubmed/31463937
http://dx.doi.org/10.1002/mp.13793

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