Pregled bibliografske jedinice broj: 1148122
Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network
Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network // 2021 International Symposium ELMAR
Zadar, Hrvatska: Institute of Electrical and Electronics Engineers (IEEE), 2021. str. 87-90 doi:10.1109/elmar52657.2021.9550936 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), ostalo)
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Naslov
Epicardial Adipose Tissue Segmentation from CT Images with A
Semi-3D Neural Network
Autori
Benčević, Marin ; Habijan, Marija ; Galić, Irena
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), ostalo
Izvornik
2021 International Symposium ELMAR
/ - : Institute of Electrical and Electronics Engineers (IEEE), 2021, 87-90
Skup
63rd International Symposium ELMAR-2021
Mjesto i datum
Zadar, Hrvatska, 13.10.2021. - 15.10.2021
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Cardiovascular imaging ; Deep neural networks ; Epicardial adipose tissue ; Medical image processing ; Semantic segmentation ;
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Projekti:
UIP-2017-05-4968 - Metode za interpretaciju medicinskih snimki za detaljnu analizu zdravlja srca (IMAGINEHEART) (Galić, Irena, HRZZ - 2017-05) ( CroRIS)
Ustanove:
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek
