The lengthy time needed for manual landmarking has delayed the widespread adoption of threedimensional (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.
