Publication: A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images
Issued Date
2021-01-01
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ISSN
23765992
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2-s2.0-85123454451
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Mahidol University
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SCOPUS
Bibliographic Citation
PeerJ Computer Science. Vol.7, (2021)
Suggested Citation
Thanongchai Siriapisith, Worapan Kusakunniran, Peter Haddawy A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images. PeerJ Computer Science. Vol.7, (2021). doi:10.7717/peerj-cs.806 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/76717
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Title
A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images
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Abstract
Epicardial fat (ECF) is localized fat surrounding the heart muscle or myocardium and enclosed by the thin-layer pericardium membrane. Segmenting the ECF is one of the most difficult medical image segmentation tasks. Since the epicardial fat is infiltrated into the groove between cardiac chambers and is contiguous with cardiac muscle, segmentation requires location and voxel intensity. Recently, deep learning methods have been effectively used to solve medical image segmentation problems in several domains with state-of-the-art performance. This paper presents a novel approach to 3D segmentation of ECF by integrating attention gates and deep supervision into the 3D U-Net deep learning architecture. The proposed method shows significant improvement of the segmentation performance, when compared with standard 3D U-Net. The experiments show excellent performance on non-contrast CT datasets with average Dice scores of 90.06%. Transfer learning from a pre-trained model of a non-contrast CT to contrast-enhanced CT dataset was also performed. The segmentation accuracy on the contrast-enhanced CT dataset achieved a Dice score of 88.16%.