# Deep Reinforcement Learning Method for 3D-CT
## 1 Introduction  
Artificial intelligence has developed rapidly in recent years. It is one of the important development directions in the medical imaging industry to apply AI's sensory cognition and deep learning technology to the field of medical imaging so as to improve the accuracy and efficiency of radiologists' diagnoses and reduce the misdiagnosis rate. In medical image analysis, effective localization of lesion areas is a key step for subsequent diagnosis, treatment, and other processes. This requires a robust and accurate method to locate feature points. Traditional supervised deep learning has several drawbacks in this regard: It requires a large amount of manually annotated data, which requires high costs. Moreover, it is very sensitive to subtle variations in image quality and lacks robustness. [PERSON] et al. showed that adding only a small amount of imperceptible image noise can cause deviations in SDL predictions [1]. Finally, traditional supervised learning algorithms have poor transparency, and their principles are more difficult for doctors to understand [2]. To address the aforementioned shortcomings, reinforcement learning can provide some solutions. First, since the RL learning environment has a richer structure, the RL model can achieve good results on fewer annotated images [3]. Second, the goal of RL learning is provided through a reward system, which makes it more stable during the training process. Finally, the reward structure provides the rationale for algorithms to make certain predictions. It also provides an opportunity for imaging medical experts to exploit their domain knowledge to help craft algorithms [3]. Nasopharyngeal cancer is one of the high incidence malignant tumors in China. Studies [4] have shown that Chinesenasopharyngeal cancer accounts for 38.29% and 40.14% of the global nasopharyngeal cancer incidence and mortality, respectively, with a 5-year relative survival rate of 43.8%. [PERSON] et al. [5] investigated a new potential mechanism for increasing the risk of distant metastasis in nasopharyngeal carcinoma patients to achieve more accurate personalized management of nasopharyngeal carcinoma cases. Therefore, for the problems faced by medical image analysis and nasopharyngeal carcinoma analysis, the reinforcement learning method is a promising development direction. **Related work**: [PERSON] et al. [6] proposed a regression forest-based method for detecting anatomical markers in whole-body CT scans. Although this method is fast, its accuracy is low when dealing with large organs. [PERSON] et al. [7] extended [PERSON] et al. [6]'s work by adding statistical shape prior values obtained through cascading regression from segmented masks. [PERSON] et al. [8] proposed a novel approach to automatically align the range image of the patient with preoperative CT scans, leveraging the contextual information of medical images to resolve data ambiguities and improve robustness. In order to address the limitations of previous detection work, autonomous feature detection and localization based on reinforcement learning have received increasing attention in recent years. [PERSON] et al. [9] showed that DRL can be used for object detection and localization. [PERSON] et al. [10] proposed a method for detecting breast lesions using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). [PERSON] et al. [11] investigated how a DRL agent was used to locate two standard transthalamic (TT) and transcerebellar (TC) planes in the three-dimensional ultrasound volume of the fetal head. They exploited prior information about the standard planes from the atlas and provided the agent with an effective warm starting point. [PERSON] et al. [12] proposed a new learning method that follows different paradigms by modeling both appearance and parameter search strategies as behavioral tasks for artificial intelligence agents. This method combines the advantages of behavioral learning achieved through reinforcement learning and effective hierarchical feature extraction achieved through deep learning.