3D cephalometric landmark detection by multiple stage deep reinforcement learning
Abstract The lengthy time needed for manual landmarking has delayed the widespread adoption of three-dimensional (3D) cephalometry. We here propose an automatic 3D cephalometric annotation system based on multi-stage deep reinforcement learning (DRL) and volume-rendered imaging. This system consider...
Guardado en:
Autores principales: | Sung Ho Kang, Kiwan Jeon, Sang-Hoon Kang, Sang-Hwy Lee |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/14c3583b99004793a05cc73915f23c71 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Three-Dimensional Cephalometric Landmarking and Frankfort Horizontal Plane Construction: Reproducibility of Conventional and Novel Landmarks
por: Gauthier Dot, et al.
Publicado: (2021) -
Author Correction: Fully Automatic System for Accurate Localisation and Analysis of Cephalometric Landmarks in Lateral Cephalograms
por: Claudia Lindner, et al.
Publicado: (2021) -
Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs
por: Yu-Cheng Yeh, et al.
Publicado: (2021) -
2-step deep learning model for landmarks localization in spine radiographs
por: Andrea Cina, et al.
Publicado: (2021) -
Deep imitation reinforcement learning for self‐driving by vision
por: Qijie Zou, et al.
Publicado: (2021)