Distributional stochastic Planner‐Actor‐Critic for deformable image registration
Abstract
Abstract Image registration is a fundamental task in medical image analysis, and large deformation registration poses significant challenges due to substantial appearance differences between the fixed image and the moving image. With the advancement of artificial intelligence, reinforcement learning has been gradually incorporated into image registration tasks. Notably, Ziwei Luo, Jing Hu, and other researchers introduced the first reinforcement learning‐based large deformation registration model called Stochastic Planner‐Actor‐Critic (SPAC), achieving superior performance in large deformation registration compared with advanced deep learning registration methods, which is the only large deformation registration reinforcement learning model. However, the existing reinforcement learning model for large deformation registration do not consider the impact of Q ‐value prediction accuracy on the overall task results. This study aims to investigate brain large deformation registration under the influence of Alzheimer's disease. To further improve the accuracy of brain large deformation registration, this paper combines the proven advanced Q ‐value distribution prediction in reinforcement learning with the large deformation registration model SPAC, designing a distributional SPAC model (DSPAC). Subsequent experiments on the ADNI dataset demonstrate the effectiveness of this model.