A Deep Reinforced Tree-Traversal Agent for Coronary Artery Centerline Extraction

Abstract

Vessel centerline extraction is fundamental for plentiful medical applications. Majority of current methods require pre-segmentations, distance maps or similar sorts of scanning whole volume action and followed by minimal-path or skeletonization algorithms. In this paper, we demonstrate a deep reinforced tree-traversal agent that automatically traces tree-structure centerlines assuming no post-prune or post-merging. It takes raw images as input and generates tree-structure centerlines naturally. To this end, road mark and dynamic reward mechanisms are proposed to make tree-structure vessels learnable and impart the agent how to learn correspondingly. Besides, a multi-task discriminator is raised to simultaneously detect bifurcations and decide terminations. We experimentally show that traced centerlines have an overlap of more than 90% and a distance less than 0.25 mm with annotated reference centerlines on coronary arteries. Beyond the promising accuracy, the proposed method also surpasses other existing methods by a large margin in terms of the time and memory efficiency. And a flexible trade-off between accuracy and time efficiency is exhibited at the inference. Codes are available at https://github.com/LzVv123456/Deep-Reinforced-Tree-Traversal.