A semi-automatic approach for epicardial adipose tissue segmentation and quantification on cardiac CT scans
Open accessAbstract
Many studies have shown that epicardial fat is associated with a higher risk of heart diseases. Accurate epicardial adipose tissue quantification is still an open research issue. Considering that manual approaches are generally user-dependent and time-consuming, computer-assisted tools can considerably improve the result repeatability as well as reduce the time required for performing an accurate segmentation. Unfortunately, fully automatic strategies might not always identify the Region of Interest (ROI) correctly. Moreover, they could require user interaction for handling unexpected events. This paper proposes a semi-automatic method for Epicardial Fat Volume (EFV) segmentation and quantification. Unlike supervised Machine Learning approaches, the method does not require any initial training or modeling phase to set up the system. As a further key novelty, the method also yields a subdivision into quartiles of the adipose tissue density. Quartile-based analysis conveys information about fat densities distribution, enabling an in-depth study towards a possible correlation between fat amounts, fat distribution, and heart diseases. Experimental tests were performed on 50 Calcium Score (CaSc) series and 95 Coronary Computed Tomography Angiography (CorCTA) series. Area-based and distance-based metrics were used to evaluate the segmentation accuracy, by obtaining Dice Similarity Coefficient (DSC) = 93.74% and Mean Absolute Distance (MAD) = 2.18 for CaSc, as well as DSC = 92.48% and MAD = 2.87 for CorCTA. Moreover, the Pearson and Spearman coefficients were computed for quantifying the correlation between the ground-truth EFV and the corresponding automated measurement, by obtaining 0.9591 and 0.9490 for CaSc, and 0.9513 and 0.9319 for CorCTA, respectively. In conclusion, the proposed EFV quantification and analysis method represents a clinically useable tool assisting the cardiologist to gain insights into a specific clinical scenario and leading towards personalized diagnosis and therapy.
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Militello et al. proposed a CDSS for epicardial fat volume quantification that used visualization representations to provide explanations and a user-centered graphical user interface design for safe interaction and integration into clinical workflow.
“Militello et al. [121] Epicardial fat volume A user-centred Graphical User Interface (GUI) design is used to allow for safe interaction of the physician as well as for an effective integration into the existing clinical workflow”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [121]
Explainability of the support system can improve the acceptability of CDSS by clinicians, increase the chances of the complex AI systems' adoption and clinical feasibility of a novel CDSS.
“Therefore, explainability of the support system can improve the acceptability of CDSS by clinicians [48] increase the chances of the complex AI systems' adoption and clinical feasibility of a novel CDSS [105,121].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [121]
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- Authors
- Militello, C., Rundo, L., Toia, P., Conti, V., Russo, G., Filorizzo, C., Maffei, E., Cademartiri, F., La Grutta, L., Midiri, M., & Vitabile, S.
- Affiliations
- Institute of Molecular Bioimaging and Physiology (IT)
- University of Cambridge (GB)
- University of Palermo (IT)
- Università degli Studi di Enna Kore (IT)
- Corresponding author
- Militello, Carmelo
- ORCID
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 114
- Pages
- 103424
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W2972051333
- Cited by
- 71
- References
- 58
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 5 · 2023: 12 · 2022: 10 · 2021: 29 · 2020: 3
- FWCI
- 7.58
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed (publishedVersion)
- Nova Science Publishers (Nova Science Publishers, Inc.) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Disease and Adiposity
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Keywords
- epicardial adipose tissue, epicardial fat volume, semi-automatic segmentation, cardiac CT, coronary CT angiography, calcium score, coronary artery disease, computer-aided diagnosis
- MeSH terms
- Deep Learning, Adipose Tissue, Adult, Algorithms, Female, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Pericardium, Tomography, X-Ray Computed
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