Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography

Deep learning allows automatic segmentation of teeth on cone beam computed tomography (CBCT). However, the segmentation performance of deep learning varies among different training strategies. Our aim was to propose a 3.5D U-Net to improve the performance of the U-Net in segmenting teeth on CBCT. Th...

Descripción completa

Detalles Bibliográficos
Autores principales: Hsu, Kang, Yuh, Da-Yo, Lin, Shao-Chieh, Lyu, Pin-Sian, Pan, Guan-Xin, Zhuang, Yi-Chun, Chang, Chia-Ching, Peng, Hsu-Hsia, Lee, Tung-Yang, Juan, Cheng-Hsuan, Juan, Cheng-En, Liu, Yi-Jui, Juan, Chun-Jung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9672125/
https://www.ncbi.nlm.nih.gov/pubmed/36396696
http://dx.doi.org/10.1038/s41598-022-23901-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9672125/
https://www.ncbi.nlm.nih.gov/pubmed/36396696
http://dx.doi.org/10.1038/s41598-022-23901-7

Ejemplares similares