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...

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Autores principales: Sung Ho Kang, Kiwan Jeon, Sang-Hoon Kang, Sang-Hwy Lee
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/14c3583b99004793a05cc73915f23c71
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