Robust Automatic Multiple Landmark Detection

Reinforcement learning (RL) has proven to be a powerful tool for automatic single landmark detection in 3D medical images. In this work, we extend RL-based single landmark detection to detect multiple landmarks simultaneously in the presence of missing data in the form of defaced 3D head MR images. Our purposed technique is both time-efficient and robust to missing data. We demonstrate that adding auxiliary landmarks can improve the accuracy and robustness of estimating primary target landmark locations. The multi-agent deep Q-network (DQN) approach described here detects landmarks within 2mm, even in the presence of missing data.