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Published on: August 28, 2018
Automatic extraction and stenosis evaluation of coronary arteries in invasive coronary angiograms
Chen Zhao1, Aviral Vij2, Saurabh Malhotra2
1Department of Applied Computing, Michigan Technological University, Houghton, MI, 49931, USA.
Insights
This study presents a deep learning method for automatic coronary artery segmentation and stenosis detection in invasive coronary angiography (ICA). The approach shows promise for improving coronary artery disease (CAD) diagnosis and treatment planning.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Cardiovascular Disease
Background:
- Coronary artery disease (CAD) is a leading cause of death and healthcare expense in the US.
- Accurate segmentation of coronary arteries and stenosis detection from invasive coronary angiography (ICA) are critical for clinical decision-making.
Purpose of the Study:
- To develop an automated deep learning method for coronary artery extraction from ICAs.
- To enable the detection of arterial stenosis using the extracted coronary artery information.
Main Methods:
- A deep learning model integrating a feature pyramid with U-Net++ was developed for coronary artery segmentation.
- A compound loss function (Dice loss, dilated Dice loss, L2 regularization) was used for training.
- A subsequent algorithm for centerline extraction, diameter calculation, and stenosis measurement was implemented.
Main Results:
- The segmentation model achieved a Dice score of 0.8899, sensitivity of 0.8595, and specificity of 0.9960 on 314 ICAs.
- The stenosis detection algorithm reported a true positive rate of 0.6840 and a positive predictive value of 0.6998 for all stenosis types.
Conclusions:
- The developed deep learning method demonstrates significant potential for clinical application in CAD diagnosis.
- This automated approach can provide valuable auxiliary information for guiding CAD treatment strategies.
Background:
Coronary artery disease (CAD) is the leading cause of death in the United States (US) and a major contributor to healthcare cost. Accurate segmentation of coronary arteries and detection of stenosis from invasive coronary angiography (ICA) are crucial in clinical decision making.
Purpose:
We aim to develop an automatic method to extract coronary arteries by deep learning and detect arterial stenosis from ICAs.
Methods:
In this study, a deep learning model which integrates a feature pyramid with a U-Net++ model was developed to automatically segment coronary arteries in ICAs. A compound loss function which contains Dice loss, dilated Dice loss, and L2 regularization was utilized to train the proposed segmentation model. Following the segmentation, an algorithm which extracts vascular centerlines, calculates the diameters, and measures the stenotic levels, was developed to detect arterial stenosis.
Results And Conclusions:
In the dataset consisting of 314 ICAs obtained from 99 patients, the segmentation model achieved an average Dice score of 0.8899, a sensitivity of 0.8595, and a specificity of 0.9960. In addition, the stenosis detection algorithm achieved a true positive rate of 0.6840 and a positive predictive value of 0.6998 on all types of stenosis, which has great promise to advance to clinical uses and could provide auxiliary suggestions for CAD diagnosis and treatment.
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