BEA-CACE: branch-endpoint-aware double-DQN for coronary artery centerline extraction in CT angiography images.

Abstract

Purpose: In order to automate the centerline extraction of the coronary tree, three challenges must be addressed: tracking branches automatically, passing through plaques successfully, and detecting endpoints accurately. This study aims to develop a method to solve the three challenges.

Methods: We propose a branch-endpoint-aware coronary centerline extraction framework. The framework consists of a deep reinforcement learning-based tracker and a 3D dilated CNN-based detector. The tracker is designed to predict the actions of an agent with the objective of tracking the centerline. The detector identifies bifurcation points and endpoints, assisting the tracker in tracking branches and terminating the tracking process automatically. The detector can also estimate the radius values of the coronary artery.

Results: The method achieves the state-of-the-art performance in both the centerline extraction and radius estimate. Furthermore, the method necessitates minimal user interaction to extract a coronary tree, a feature that surpasses other interactive methods.

Conclusion: The method can track branches automatically, pass through plaques successfully and detect endpoints accurately. Compared with other interactive methods that require multiple seeds, our method only needs one seed to extract the entire coronary tree.