Abstract
The epicardial fat plays a key role in the development of many cardiovascular diseases. It is necessary and useful to precisely segment this fat from CT scans in clinical studies. However, it is not feasible to manually segment this fat in clinical practice, as the workload and cost for technicians or physicians is quite high. In this work, we propose a novel method for automatic segmentation and quantification of epicardial fat from CT scans accurately. In detail, dual U-Nets with the morphological processing layer is used for this goal. The first network is based on the U-Net framework to detect the pericardium, before segmenting its inside region. A morphological layer is concatenated as the following layer of the first network, to refine and obtain the ideal inside region of the pericardium. While the second network is also applied using U-Net as its backbone to find and segment the epicardial fat of the processed inside region from the pericardium using the first network. Our proposed method obtains the highest mean Dice similarity (91.19%), correlation coefficient (0.9304) compared to other state-of-art methods on a cardiac CT dataset with 20 patients. The results indicate our proposed method is effective for quantifying epicardial fat automatically.
Highlights
The epicardial fat is a local visceral fat deposit, often existing between the pericardium and the myocardium, which can surround the coronary arteries as well as the entire heart directly [1], [2]
To segment and quantify the epicardial fat precisely, here, we propose a novel method based on the U-Net framework, applying dual U-Nets with a morphological layer on cardiac CT scans
A cardiac CT scans dataset is first described before being applied for evaluating the performance of the proposed method
Summary
The epicardial fat is a local visceral fat deposit, often existing between the pericardium and the myocardium, which can surround the coronary arteries as well as the entire heart directly [1], [2]. Authors believe that epicardial fat can cause the formation of coronary atherosclerosis directly through the local generation of the inflammatory factors [3], [4]. Researchers have found that the epicardial fat can play a dominant role in the coronary artery disease [5]. In [6], the epicardial fat is regarded as a predictor for heart disease events, compared to other common risk factors by applying 998 candidates in a MESA study. Performing segmentation and predicting the volumes of epicardial fat is a useful and interesting task for evaluating the risk of heart
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