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A framework of myocardial bridge detection with x-ray angiography sequence
Peng Zhou1, Guangpu Wang1, Shuo Wang2
1The School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Nankai District, No.92 Weijin Road, Tianjin, China.
Insights
This study introduces a new AI framework for automatically detecting myocardial bridges and coronary artery stenosis using x-ray angiography. The method shows promise for improving the diagnosis of these critical cardiovascular conditions.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Myocardial bridges are congenital abnormalities where heart muscle covers coronary arteries, potentially causing ischemia and sudden death.
- Automatic diagnosis of myocardial bridges is challenging.
Purpose of the Study:
- To develop a novel framework for automatic detection of myocardial bridges and vessel stenosis using x-ray angiography sequences.
- To improve diagnostic accuracy for coronary artery abnormalities.
Main Methods:
- A novel neural network combining CNNs and transformers for coronary vessel segmentation.
- Vessel tree description and inter-sequence information fusion.
- Automatic myocardial bridge detection based on vessel stenosis identification.
Main Results:
- Achieved Dice score of 0.917 and ASD of 1.39 for vessel segmentation.
- Attained 92.7% accuracy in detecting 262 stenoses.
- Reached 75% accuracy in myocardial bridge detection with multi-frame processing.
Conclusions:
- The proposed algorithm successfully automates the detection of stenosis and myocardial bridges.
- This framework offers a new approach for the automated diagnosis of coronary artery diseases.
Background:
Myocardial bridges are congenital anatomical abnormalities in which myocardium covers a segment of coronary arteries, leading to stenocardia, myocardial ischemia, and sudden cardiac death in severe cases. However, automatic diagnosis of myocardial bridge presents significant challenges.
Method:
A novel framework of myocardial bridge detection with x-ray angiography sequence is proposed, which can realize automatic detection of vessel stenosis and myocardial bridge. Firstly, we employ a novel neural network model for coronary vessel segmentation, which consists of both CNNs and transformer structures to effectively extract both local and global information of the vessels. Secondly, we describe the vessel segment information, establish the vessel tree in the image, and fuse the vessel tree information between sequences. Finally, based on vessel stenosis detection, we realize automatic detection of the myocardial bridge by querying the blood vessels between the image sequence information.
Results:
In experiment, we evaluate the segmentation results using two metrics, Dice and ASD, and achieve scores of 0.917 and 1.39, respectively. In the stenosis detection, we achieve an average accuracy rate of 92.7% in stenosis detection among 262 stenoses. In multi-frame image processing, vessels in different frames can be well-matched, and the accuracy of myocardial bridge detection achieves 75%.
Conclusions:
Our experimental results demonstrate that the algorithm can automatically detect stenosis and myocardial bridge, providing a new idea for subsequent automatic diagnosis of coronary vessels.
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