Automated Cephalometric Landmark Detection Using Deep Reinforcement Learning

Abstract

Accurate cephalometric landmark detection leads to accurate analysis, diagnosis, and surgical planning. Many studies on automated landmark detection have been conducted, however reinforcement learning-based networks have not yet been applied. This is the first study to apply deep Q-network (DQN) and double deep Q-network (DDQN) to automated cephalometric landmark detection to the best of our knowledge. The performance of the DQN-based network for cephalometric landmark detection was evaluated using the IEEE International Symposium of Biomedical Imaging (ISBI) 2015 Challenge data set and compared with the previously proposed methods. Furthermore, the clinical applicability of DQN-based automated cephalometric landmark detection was confirmed by testing the DQN-based and DDQN-based network using 500-patient data collected in a clinic. The DQN-based network demonstrated that the average mean radius error of 19 landmarks was smaller than 2 mm, that is, the clinically accepted level, without data augmentation and additional preprocessing. Our DQN-based and DDQN-based approaches tested with the 500-patient data set showed the average success detection rate of 67.33% and 66.04% accuracy within 2 mm, respectively, indicating the feasibility and potential of clinical application.