Deep Learning Approach to Gated Coronary Artery Calcium Scan Segmentations

The work done in this paper highlights the use of Tversky loss in small segmentations and using an ensemble model to achieve higher accuracy.

Coronary artery calcium (CAC) tests are a CT scan of the heart that takes detailed images of arteries to locate deposits of calcium. Excessive calcium in the arteries can lead to reduced blood flow to the heart and cause heart diseases. These are important indicators for heart attacks and diseases and getting CAC scans are useful in identifying high-risk individuals. Agatson scores are the calculations of total calcium in the heart, state of the art programs are currently about 95 percent accurate. The work done in this paper highlights the use of Tversky loss in small segmentations and using an ensemble model to achieve higher accuracy.

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Deep Learning Approach to Gated Coronary Artery Calcium Scan Segmentations

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Research Paper