Deep Reinforcement Learning Method for 3D-CT Nasopharyngeal Cancer Localization with Prior Knowledge

Abstract

Fast and accurate lesion localization is an important step in medical image analysis. The current supervised deep learning methods have obvious limitations in the application of radiology, as they require a large number of manually annotated images. In response to the above issues, we introduced a deep reinforcement learning (DRL)-based method to locate nasopharyngeal carcinoma lesions in 3D-CT scans. The proposed method uses prior knowledge to guide the agent to reasonably reduce the search space and promote the convergence rate of the model. Furthermore, the multi-scale processing technique is also used to promote the localization of small objects. We trained the proposed model with 3D-CT scans of 50 patients and evaluated it with 3D-CT scans of 30 patients. The experimental results showed that the proposed model has strong robustness, and its accuracy was improved by more than 1 mm on average under the premise of using a smaller dataset compared with the DQN models in recent studies. The proposed model could effectively locate the lesion area of nasopharyngeal carcinoma in 3D-CT scans.