[2204.12904] Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network
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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2204.12904 (eess)
[Submitted on 27 Apr 2022]

Title:Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network

Authors:Marin Benčević, Marija Habijan, Irena Galić
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Abstract:Epicardial adipose tissue is a type of adipose tissue located between the heart wall and a protective layer around the heart called the pericardium. The volume and thickness of epicardial adipose tissue are linked to various cardiovascular diseases. It is shown to be an independent cardiovascular disease risk factor. Fully automatic and reliable measurements of epicardial adipose tissue from CT scans could provide better disease risk assessment and enable the processing of large CT image data sets for a systemic epicardial adipose tissue study. This paper proposes a method for fully automatic semantic segmentation of epicardial adipose tissue from CT images using a deep neural network. The proposed network uses a U-Net-based architecture with slice depth information embedded in the input image to segment a pericardium region of interest, which is used to obtain an epicardial adipose tissue segmentation. Image augmentation is used to increase model robustness. Cross-validation of the proposed method yields a Dice score of 0.86 on the CT scans of 20 patients.
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2204.12904 [eess.IV]
  (or arXiv:2204.12904v1 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2204.12904
Journal reference: 2021 International Symposium ELMAR, 2021, pp. 87-90
Related DOI: https://doi.org/10.1109/ELMAR52657.2021.9550936

Submission history

From: Marin Benčević [view email]
[v1] Wed, 27 Apr 2022 13:15:44 UTC (3,707 KB)
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