Key result
The proposed dual U-Nets with a morphological processing layer achieved a mean Dice similarity score of 91.19% and a correlation coefficient of 0.9304 for automatic epicardial fat segmentation.
Why the study?
Epicardial fat plays a key role in cardiovascular diseases, but manual segmentation on CT scans is not feasible in clinical practice due to high workload and cost.
A novel dual U-Net architecture with a morphological processing layer provides highly accurate, automated segmentation and quantification of epicardial fat from cardiac CT scans, outperforming standard deep learning models.
May ease manual epicardial fat segmentation workload; leaves open prospective validation before clinical adoption.
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.
No takes yet. Share an insight, caveat, or question.
Zhang et al. (2020) studied Epicardial fat segmentation (n=20). Dual U-Nets with a morphological processing layer vs. FCN, U-Net, Seg-Net was evaluated on Mean Dice similarity score. The proposed dual U-Nets with a morphological processing layer achieved a mean Dice similarity score of 91.19% and a correlation coefficient of 0.9304 for automatic epicardial fat segmentation.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: