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Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection

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High-quality annotations for deep learning enabled plaque analysis in SCAPIS cardiac computed tomography angiography.

Erika Fagman^1,2, Jennifer Alvén^3,4, Johan Westerbergh⁵

¹Department of Radiology, Institute of Clinical Sciences, University of Gothenburg, Sweden.

|May 22, 2023

Summary

Researchers created a high-quality annotated coronary computed tomography angiography (CCTA) dataset from the SCAPIS study. This dataset shows good reproducibility and links plaque characteristics to cardiovascular risk, aiding deep learning tool development.

Keywords:

Annotated dataset Coronary Computed Tomography Angiography Coronary plaque analysis Deep Learning

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Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans

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Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans

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Published on: August 28, 2018

Area of Science:

Cardiology
Medical Imaging
Artificial Intelligence

Background:

Coronary computed tomography angiography (CCTA) aids in identifying high-risk coronary events.
Manual plaque analysis from CCTA is time-consuming and requires expert readers.
Deep learning models need large, expert-annotated datasets for training.

Purpose of the Study:

To generate a large, high-quality annotated CCTA dataset from the Swedish CArdioPulmonary BioImage Study (SCAPIS).
To report the reproducibility of plaque annotation by a core lab.
To describe plaque characteristics and their association with established cardiovascular risk factors.

Main Methods:

Manual segmentation of coronary arteries using semi-automatic software by multiple readers.
Analysis of 469 subjects with coronary plaques, stratified by Systematic Coronary Risk Evaluation (SCORE).
Reproducibility assessment for plaque detection and volume, including inter-reader agreement.

Main Results:

High agreement for plaque detection (0.91) and good reproducibility for plaque volume measurements (ICC 0.94).
Positive correlations found between SCORE and total plaque volume (rho=0.30) and low attenuation plaque volume (rho=0.29).
Generated dataset is enriched with high-risk plaques suitable for deep learning model training.

Conclusions:

A high-quality annotated CCTA dataset with good reproducibility was successfully generated.
The dataset demonstrates expected correlations between plaque features and cardiovascular risk.
The dataset is well-suited for developing and validating automated deep learning analysis tools for CCTA.