Medical object detection and segmentation are crucial pre-processing steps in the clinical workflow for diagnosis and therapy planning. Although deep learning methods have achieved considerable performance in this field, they impose several shortcomings, such as computational limitations, sub-optimal parameter optimization, and weak generalization. Deep reinforcement learning as the newest artificial intelligence algorithm has great potential to address the limitation of traditional deep learning methods, as well as obtaining accurate detection and segmentation results. Deep reinforcement learning has a cognitive-like process to propose the area of desirable objects, thereby facilitating accurate object detection and segmentation. In this thesis, we deploy deep reinforcement learning into two challenging and representative medical object detection and segmentation tasks: 1) Sequential-Conditional Reinforcement Learning (SCRL) for vertebral body detection and segmentation by modeling the spine anatomy with deep reinforcement learning; 2) Weakly-Supervised Teacher-Student network (WSTS) for liver tumor segmentation from the non-enhanced image by transferring tumor knowledge from the enhanced image with deep reinforcement learning. The experiment indicates our methods are effective and outperform state-of-art deep learning methods. Therefore, this thesis improves object detection and segmentation accuracy and offers researchers a novel approach based on deep reinforcement learning in medical image analysis
