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

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

Abstract 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...

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Bibliographic Details
Main Authors: Kang Hsu, Da-Yo Yuh, Shao-Chieh Lin, Pin-Sian Lyu, Guan-Xin Pan, Yi-Chun Zhuang, Chia-Ching Chang, Hsu-Hsia Peng, Tung-Yang Lee, Cheng-Hsuan Juan, Cheng-En Juan, Yi-Jui Liu, Chun-Jung Juan
Format: Article
Language:English
Published: Nature Portfolio 2022-11-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-022-23901-7

Internet

https://doi.org/10.1038/s41598-022-23901-7

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