Using the U-Net Family for Epicardial Adipose Tissue Segmentation and Quantification in Non-Contrast CT Article Swipe
Lu Liu
,
Runlei Ma
,
Peter M. A. van Ooijen
,
Matthijs Oudkerk
,
Rozemarijn Vliegenthart
,
Christoph Brüne
,
Raymond Veldhuis
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1530148/v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1530148/v1
Background: Epicardial adipose tissue (EAT) locates between the visceral pericardium and myocardium and the EAT volume is correlated with cardiovascular risk. Nowadays, many deep learning-based automated EAT segmentation and quantification methods in the U-net family were developed to reduce the workload for radiologists. However, most of the works were based on private or small dataset with di↵erent label types. Thus, their reproducibility is relatively low and comparison of their performance is difficult. Methods: In this work, we comparably studied and evaluated the state-of-the-art segmentation methods, and o↵ered future directions. Our work is based on a dataset of 154 non-contrast CT scans from the ROBINSCA study with two types of labels: (a) region inside the pericardium and (b) pixel-wise EAT labels. We selected four advanced methods from the U-net family: 3D U-net, 3D attention U-net, an extended 3D attention U-net, and U-net++. For evaluation, we did both four-fold cross-validation and hold-out tests. Agreement between the automatic segmentation/quantification and the manual quantification was evaluated with the Pearson correlation and the Bland-Altman analysis.Results: Generally, the models trained with label type (a) showed better performance compared to models trained with label type (b). The U-net++ model trained with label type (a) showed the best performance of segmentation and quantification. The U-net++ model trained with label type (a) efficiently provides better EAT segmentation results(Hold-out test: DCS=80.18 ± 0.20%, mIoU=67.13 ± 0.39%, sensitivity=81.47 ± 0.43%, specificity=99.64 ± 0.00%, Pearson correlation=0.9405) and EAT volume compared to the other U-net-based networks and the recent EAT segmentation method.Conclusions: 3D convolutional neural networks do not always perform better than 2D convolutional neural networks in the EAT segmentation and quantification.And labels of the region inside the pericardium are helpful to train more accurate EAT segmentation models. Deep learning-based methods have the potential to provide good EAT segmentation and quantification.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-1530148/v1
- https://www.researchsquare.com/article/rs-1530148/latest.pdf
- OA Status
- green
- Cited By
- 1
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4229017017
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4229017017Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-1530148/v1Digital Object Identifier
- Title
-
Using the U-Net Family for Epicardial Adipose Tissue Segmentation and Quantification in Non-Contrast CTWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-05-05Full publication date if available
- Authors
-
Lu Liu, Runlei Ma, Peter M. A. van Ooijen, Matthijs Oudkerk, Rozemarijn Vliegenthart, Christoph Brüne, Raymond VeldhuisList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-1530148/v1Publisher landing page
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https://www.researchsquare.com/article/rs-1530148/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-1530148/latest.pdfDirect OA link when available
- Concepts
-
Contrast (vision), Adipose tissue, Epicardial adipose tissue, Segmentation, Epicardial fat, Internal medicine, Medicine, Artificial intelligence, Computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1Per-year citation counts (last 5 years)
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35Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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