Automatic Detection of the Retina in Optical Coherence Tomography using Deep Q Learning

This study presents a novel approach to detecting the retina in optical coherence tomography (OCT) images using Deep Q learning. The proposed method uses an agent to extract contextual information from the input OCT to produce a tight-bounding box around the retina in a step-wise fashion. The detection task implements a decision process governed by a reinforcement learning strategy, where the agent takes actions and receives rewards according to their outcome. During the localization process, the agent learns the optimal set of actions to complete the detection task using a Q-network that estimates the value of the expected return of an action at any given step. Experiments on a test OCT dataset of 100 images showed that the proposed method accurately located the retina with a mean recall of 0.988 and a mean F1 score of 0.94.