Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

Automatic aortic valve area detection in echocardiography images using convolutional neural networks and U-net architecture for bicuspid aortic valve recognition

Giannakaki Aikaterini-Antonia, Moirogiorgou Konstantia, Zervakis Michail, Anousakis-Vlachochristou Nikolaos, Matsopoulos, George K, Komporozos Christoforos, Sourides Vasileios, Katsimagklis Georgios, Drakopoulou Maria, Toutouzas, Konstantinos, Avgeropoulou Catherine, Androulakis Aristeidis

Simple record

URIhttp://purl.tuc.gr/dl/dias/A5DF1B62-C7D1-480E-A572-3FA35330DBEE-
Identifierhttps://doi.org/10.1109/IST50367.2021.9651398-
Identifierhttps://ieeexplore.ieee.org/document/9651398-
Languageen-
Extent6 pagesen
TitleAutomatic aortic valve area detection in echocardiography images using convolutional neural networks and U-net architecture for bicuspid aortic valve recognitionen
CreatorGiannakaki Aikaterini-Antoniaen
CreatorΓιαννακακη Αικατερινη-Αντωνιαel
CreatorMoirogiorgou Konstantiaen
CreatorΜοιρογιωργου Κωνσταντιαel
CreatorZervakis Michailen
CreatorΖερβακης Μιχαηλel
CreatorAnousakis-Vlachochristou Nikolaosen
CreatorMatsopoulos, George Ken
CreatorKomporozos Christoforosen
CreatorSourides Vasileiosen
CreatorKatsimagklis Georgiosen
CreatorDrakopoulou Mariaen
CreatorToutouzas, Konstantinosen
CreatorAvgeropoulou Catherineen
CreatorAndroulakis Aristeidisen
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryAutomatic methods for heart disease recognition are a promising asset in precise diagnosis and prevention of complications. Regarding bicuspid aortic valve, for which this field is still limited, accurate aortic valve detection would be an essential step in the procedure of using the most common testing method, echocardiography, to automatically detect this malformation. In this study, we propose using a convolutional neural network with U-net architecture for demarcating the aortic valve area in echocardiography images, as an initial step in automatic bicuspid aortic valve detection. Our model achieved a prediction accuracy of 97%, sensitivity 94%, specificity 98% and Intersection over Union 87%.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2023-05-11-
Date of Publication2021-
SubjectEchocardiographyen
SubjectConvolutional neural networksen
SubjectU-neten
SubjectAortic valveen
SubjectBicuspid aortic valveen
Bibliographic CitationK. Giannakaki, K. Moirogiorgou, M. Zervakis, N. Anousakis-Vlachochristou, G. K. Matsopoulos, C. Komporozos, V. Sourides, G. Katsimagklis, M. Drakopoulou, K. Toutouzas, C. Avgeropoulou and A. Androulakis, "Automatic aortic valve area detection in echocardiography images using convolutional neural networks and U-net architecture for bicuspid aortic valve recognition," presented at the 2021 IEEE International Conference on Imaging Systems and Techniques (IST), Kaohsiung, Taiwan, 2021, doi: 10.1109/IST50367.2021.9651398.en

Services

Statistics

 

Copyright © DIAS 2013