Enhancing Anatomical Landmark Detection in Brain Images through Multiagent Deep Reinforcement Learning

Identifying anatomical landmarks in medical imaging is critical for accurate diagnosis and treatment planning. However, manual annotation varies widely among observers and demands specialized expertise. We propose an innovative approach using multi-agent reinforcement learning, based on the understanding that anatomical landmarks are interconnected and exhibit non-random configurations. Our framework employs a Deep Q-Network architecture with interconnected agents that collaborate by sharing knowledge during training, improving accuracy. It supports multiple agents, each tasked with identifying specific landmarks, and achieves a 50% reduction in detection errors compared to existing methods, while requiring fewer computational resources and less training time. This technology holds promise for advancing anatomical landmark recognition in brain imaging, facilitating more precise medical image interpretation.