Key result
Semi-automatic epicardial fat quantification matches manual processing, needing minimal user intervention in ~63% of non-contrast CTs.
Why the study?
Does a semi-automatic algorithm accurately quantify epicardial fat in non-contrasted CT images compared to manual measurement?
Does a semi-automatic algorithm accurately quantify epicardial fat in non-contrasted CT images compared to manual measurement?
A semi-automatic algorithm can quantify epicardial fat in non-contrasted CT images with minimal user intervention in most cases, offering a potential alternative to manual measurement.
May streamline epicardial fat quantification in non-contrast CT; leaves open clinical utility and prospective validation.
In this work, we present a technique to semi-automatically quantify the epicardial fat in non-contrasted computed tomography (CT) images. The epicardial fat is very close to the pericardial fat, being separated only by the pericardium that appears in the image as a very thin line, which is hard to detect. Therefore, an algorithm that uses the anatomy of the heart was developed to detect the pericardium line via control points of the line. From the points detected an interpolation was applied based on the cubic interpolation, which was also improved to avoid incorrect interpolation that occurs when the two variables are non-monotonic. The method is validated by using a set of 40 CT images of the heart of 40 human subjects. In 62.5% of the cases only minimal user intervention was required and the results compared favourably with the results obtained by the manual process.
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Barbosa et al. (2011) studied this question. Semi-automatic quantification algorithm for epicardial fat vs. Manual process was evaluated on Minimal user intervention required. A semi-automatic algorithm for quantifying epicardial fat in non-contrasted CT images required only minimal user intervention in 62.5% of cases and compared favorably with manual processing.
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