Deep Reinforcement Learning for Vessel Centerline Tracing in Multi-modality 3D Volumes

Accurate vessel centerline tracing greatly benefits vessel centerline geometry assessment and facilitates precise measurements of vessel diameters and lengths. However, cursive and longitudinal geometries of vessels make centerline tracing a challenging task in volumetric images. Treating the problem with traditional feature handcrafting is often adhoc and time-consuming, resulting in suboptimal solutions. In this work, we propose a unified end-to-end deep reinforcement learning approach for robust vessel centerline tracing in multi-modality 3D medical volumes. Instead of time-consuming exhaustive search in 3D space, we propose to learn an artificial agent to interact with surrounding environment and collect rewards from the interaction. A deep neural network is integrated to the system to predict stepwise action value for every possible actions. With this mechanism, the agent is able to probe through an optimal navigation path to trace the vessel centerline. Our proposed approach is evaluated on a dataset of over 2,000 3D volumes with diverse imaging modalities, including contrasted CT, non-contrasted CT, C-arm CT and MR images. The experimental results show that the proposed approach can handle large variations from vessel shape to imaging characteristics, with a tracing error as low as 3.28mm and detection time as fast as 1.71 s per volume.