Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents
This addresses the need for efficient and consistent view planning in clinical imaging, though it appears incremental as it applies existing RL methods to a specific domain.
The paper tackled the problem of automatically finding standardized view planes in 3D medical images, which is tedious and operator-dependent, by proposing a multi-scale deep reinforcement learning method that achieved accuracies of 1.53mm, 1.98mm, and 4.84mm for specific brain and cardiac MRI planes.
We propose a fully automatic method to find standardized view planes in 3D image acquisitions. Standard view images are important in clinical practice as they provide a means to perform biometric measurements from similar anatomical regions. These views are often constrained to the native orientation of a 3D image acquisition. Navigating through target anatomy to find the required view plane is tedious and operator-dependent. For this task, we employ a multi-scale reinforcement learning (RL) agent framework and extensively evaluate several Deep Q-Network (DQN) based strategies. RL enables a natural learning paradigm by interaction with the environment, which can be used to mimic experienced operators. We evaluate our results using the distance between the anatomical landmarks and detected planes, and the angles between their normal vector and target. The proposed algorithm is assessed on the mid-sagittal and anterior-posterior commissure planes of brain MRI, and the 4-chamber long-axis plane commonly used in cardiac MRI, achieving accuracy of 1.53mm, 1.98mm and 4.84mm, respectively.