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

• URL: http://arxiv.org/abs/2204.12904v1
• Date: Wed, 27 Apr 2022 13:15:44 GMT
• Title: Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network
• Abstract summary: 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. Cross-validation of the proposed method yields a Dice score of 0.86 on the CT scans of 20 patients.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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