3D cephalometric landmark detection by multiple stage deep reinforcement learning

• Published in: Scientific reports 2021-09, Vol.11 (1), p.17509-17509, Article 17509
• Main Authors: Kang, Sung Ho, Jeon, Kiwan, Kang, Sang-Hoon, Lee, Sang-Hwy
• Format: Article
• Language: English
• Subjects: 631/114/1305; 692/700/3032/3093/3095; Computed tomography; Deep learning; Humanities and Social Sciences; multidisciplinary; Reinforcement; Science; Science (multidisciplinary); Segmentation; Training
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-97116-7

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 considers geometrical characteristics of landmarks and simulates the sequential decision process underlying human professional landmarking patterns. It consists mainly of constructing an appropriate two-dimensional cutaway or 3D model view, then implementing single-stage DRL with gradient-based boundary estimation or multi-stage DRL to dictate the 3D coordinates of target landmarks. This system clearly shows sufficient detection accuracy and stability for direct clinical applications, with a low level of detection error and low inter-individual variation (1.96 ± 0.78 mm). Our system, moreover, requires no additional steps of segmentation and 3D mesh-object construction for landmark detection. We believe these system features will enable fast-track cephalometric analysis and planning and expect it to achieve greater accuracy as larger CT datasets become available for training and testing.