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Published on: September 22, 2023
Coronary artery stenosis detection via proposal-shifted spatial-temporal transformer in X-ray angiography
Tao Han1, Danni Ai1, Xinyu Li1
1Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.
Insights
This study introduces an advanced Transformer-based framework for detecting coronary artery stenosis in X-ray angiography (XRA) images. The method effectively leverages spatio-temporal features, achieving superior accuracy in identifying coronary artery disease (CAD).
Area of Science:
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
- Medical Image Analysis
Background:
- Accurate detection of coronary artery stenosis in X-ray angiography (XRA) is vital for diagnosing and treating coronary artery disease (CAD).
- Current methods struggle with complex vascular structures, image quality issues, and lesion variability, often failing to efficiently utilize spatio-temporal information in XRA sequences.
- This limitation hinders optimal performance in stenosis detection tasks.
Purpose of the Study:
- To develop a novel framework for accurate coronary artery stenosis detection in XRA images.
- To enhance the exploitation of spatio-temporal information for improved diagnostic performance.
- To overcome the limitations of existing methods in handling complex vascular structures and image quality.
Main Methods:
- A Transformer-based module was developed to aggregate proposal-level spatio-temporal features.
- A proposal-shifted spatio-temporal tokenization (PSSTT) scheme was devised to gather region-of-interest (RoI) features within local windows.
- A Transformer-based feature aggregation (TFA) network was employed to enhance RoI features by learning long-range spatio-temporal context for stenosis prediction.
Main Results:
- The proposed method achieved a high F1 score of 90.88% on 233 XRA coronary artery sequences.
- It outperformed 15 other state-of-the-art detection methods in qualitative and quantitative experiments.
- The framework demonstrated a strong ability to aggregate spatio-temporal features for accurate stenosis detection.
Conclusions:
- The developed Transformer-based framework effectively addresses the challenge of coronary artery stenosis detection in XRA images.
- The method's ability to aggregate spatio-temporal features significantly improves detection accuracy.
- This approach offers a promising advancement for diagnosing coronary artery disease using medical imaging.
Abstract:
Accurate detection of coronary artery stenosis in X-ray angiography (XRA) images is crucial for the diagnosis and treatment of coronary artery disease. However, stenosis detection remains a challenging task due to complicated vascular structures, poor imaging quality, and fickle lesions. While devoted to accurate stenosis detection, most methods are inefficient in the exploitation of spatio-temporal information of XRA sequences, leading to a limited performance on the task. To overcome the problem, we propose a new stenosis detection framework based on a Transformer-based module to aggregate proposal-level spatio-temporal features. In the module, proposal-shifted spatio-temporal tokenization (PSSTT) scheme is devised to gather spatio-temporal region-of-interest (RoI) features for obtaining visual tokens within a local window. Then, the Transformer-based feature aggregation (TFA) network takes the tokens as the inputs to enhance the RoI features by learning the long-range spatio-temporal context for final stenosis prediction. The effectiveness of our method was validated by conducting qualitative and quantitative experiments on 233 XRA sequences of coronary artery. Our method achieves a high F1 score of 90.88%, outperforming other 15 state-of-the-art detection methods. It demonstrates that our method can perform accurate stenosis detection from XRA images due to the strong ability to aggregate spatio-temporal features.
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