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Updated: Oct 30, 2025
Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection
Published on: September 22, 2023
Calcium Identification and Scoring Based on Echocardiography. An Exploratory Study on Aortic Valve Stenosis
Luis B Elvas1,2, Ana G Almeida3, Luís Rosario3
1Inov Inesc Inovação-Instituto de Novas Tecnologias, 1000-029 Lisbon, Portugal.
Insights
Computer Vision (CV) techniques can now identify aortic valve calcium in echocardiography images, reducing the need for radiation-heavy CT scans. This method offers a semi-automatic approach for better patient monitoring and reduced medical workload.
Area of Science:
- Medical Imaging
- Computer Vision
- Cardiology
Background:
- Aortic valve calcification diagnosis currently requires expert echocardiography interpretation and radiation-intensive cardiac CT scans for quantification.
- Limitations in CT scan frequency restrict patient monitoring for aortic valve calcification.
- Computer Vision (CV) offers potential for automated image analysis to improve diagnostic efficiency.
Purpose of the Study:
- To develop and validate a CV-based technique for semi-automatic identification and quantification of aortic valve calcium from echocardiography images.
- To reduce reliance on cardiac CT scans and associated radiation exposure.
- To enhance patient monitoring capabilities for aortic valve calcification.
Main Methods:
- A novel CV technique was developed for semi-automatic calcium identification in echocardiography images.
- Echocardiographic adaptive image binarization was employed, using blood intensity as a reference for thresholding.
- Calcium regions of interest (ROIs) were interactively selected by experts, and calcium pixel count was computed.
Main Results:
- The CV technique achieved a calcium pixel count with an absolute pixel value margin of error of 3 (0-255 scale).
- A strong Pearson Correlation of 0.92 was observed between the CV-derived calcium area and human expert assessment.
- The method demonstrated robustness across images with varying acquisition settings and brightness.
Conclusions:
- The developed CV approach provides a promising, radiation-free method for semi-automatic aortic valve calcium assessment using echocardiography.
- This technique can potentially decrease medical workload and improve patient monitoring efficiency.
- Further validation may lead to wider clinical adoption for non-invasive calcium quantification.
Abstract:
Currently, an echocardiography expert is needed to identify calcium in the aortic valve, and a cardiac CT-Scan image is needed for calcium quantification. When performing a CT-scan, the patient is subject to radiation, and therefore the number of CT-scans that can be performed should be limited, restricting the patient's monitoring. Computer Vision (CV) has opened new opportunities for improved efficiency when extracting knowledge from an image. Applying CV techniques on echocardiography imaging may reduce the medical workload for identifying the calcium and quantifying it, helping doctors to maintain a better tracking of their patients. In our approach, a simple technique to identify and extract the calcium pixel count from echocardiography imaging, was developed by using CV. Based on anonymized real patient echocardiographic images, this approach enables semi-automatic calcium identification. As the brightness of echocardiography images (with the highest intensity corresponding to calcium) vary depending on the acquisition settings, echocardiographic adaptive image binarization has been performed. Given that blood maintains the same intensity on echocardiographic images-being always the darker region-blood areas in the image were used to create an adaptive threshold for binarization. After binarization, the region of interest (ROI) with calcium, was interactively selected by an echocardiography expert and extracted, allowing us to compute a calcium pixel count, corresponding to the spatial amount of calcium. The results obtained from these experiments are encouraging. With this technique, from echocardiographic images collected for the same patient with different acquisition settings and different brightness, obtaining a calcium pixel count, where pixel values show an absolute pixel value margin of error of 3 (on a scale from 0 to 255), achieving a Pearson Correlation of 0.92 indicating a strong correlation with the human expert assessment of calcium area for the same images.
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