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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

Απλή Εγγραφή

URIhttp://purl.tuc.gr/dl/dias/A5DF1B62-C7D1-480E-A572-3FA35330DBEE-
Αναγνωριστικόhttps://doi.org/10.1109/IST50367.2021.9651398-
Αναγνωριστικόhttps://ieeexplore.ieee.org/document/9651398-
Γλώσσαen-
Μέγεθος6 pagesen
ΤίτλοςAutomatic aortic valve area detection in echocardiography images using convolutional neural networks and U-net architecture for bicuspid aortic valve recognitionen
ΔημιουργόςGiannakaki Aikaterini-Antoniaen
ΔημιουργόςΓιαννακακη Αικατερινη-Αντωνιαel
ΔημιουργόςMoirogiorgou Konstantiaen
ΔημιουργόςΜοιρογιωργου Κωνσταντιαel
ΔημιουργόςZervakis Michailen
ΔημιουργόςΖερβακης Μιχαηλel
ΔημιουργόςAnousakis-Vlachochristou Nikolaosen
ΔημιουργόςMatsopoulos, George Ken
ΔημιουργόςKomporozos Christoforosen
ΔημιουργόςSourides Vasileiosen
ΔημιουργόςKatsimagklis Georgiosen
ΔημιουργόςDrakopoulou Mariaen
ΔημιουργόςToutouzas, Konstantinosen
ΔημιουργόςAvgeropoulou Catherineen
ΔημιουργόςAndroulakis Aristeidisen
ΕκδότηςInstitute of Electrical and Electronics Engineersen
ΠερίληψηAutomatic 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
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2023-05-11-
Ημερομηνία Δημοσίευσης2021-
Θεματική ΚατηγορίαEchocardiographyen
Θεματική ΚατηγορίαConvolutional neural networksen
Θεματική ΚατηγορίαU-neten
Θεματική ΚατηγορίαAortic valveen
Θεματική ΚατηγορίαBicuspid aortic valveen
Βιβλιογραφική ΑναφοράK. 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

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