BEA-CACE: branch-endpoint-aware double-DQN for coronary artery centerline extraction in CT angiography images
Yuyang Zhang1, Gongning Luo1, Wei Wang2
1Faculty of Computing, Harbin Institute of Technology, Harbin, 150001, China.
Insights
This study introduces an automated coronary centerline extraction method using deep reinforcement learning and a 3D CNN. The approach successfully tracks branches, navigates plaques, and accurately detects endpoints with minimal user input.
Area of Science:
- Medical Imaging
- Computational Cardiology
- Artificial Intelligence in Medicine
Background:
- Automating coronary tree centerline extraction is crucial for cardiovascular analysis.
- Existing methods face challenges in automatic branch tracking, plaque traversal, and endpoint detection.
Purpose of the Study:
- To develop an automated method for coronary centerline extraction.
- To address challenges in tracking branches, navigating plaques, and detecting endpoints.
Main Methods:
- A novel framework combining a deep reinforcement learning-based tracker and a 3D dilated Convolutional Neural Network (CNN) detector.
- The tracker predicts agent actions for centerline tracking.
- The detector identifies bifurcations and endpoints, aiding tracking and estimating vessel radius.
Main Results:
- Achieved state-of-the-art performance in coronary centerline extraction and radius estimation.
- Demonstrated minimal user interaction, outperforming other interactive methods.
- Successfully tracked branches, navigated plaques, and accurately detected endpoints.
Conclusions:
- The proposed method effectively automates coronary centerline extraction.
- Requires only a single seed for complete tree extraction, unlike multi-seed interactive methods.
- Offers a robust solution for accurate and efficient coronary tree analysis.
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.
More Related Videos
13:07Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
Published on: January 15, 2022
06:57Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection
Published on: September 22, 2023
