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Pregled bibliografske jedinice broj: 1085085

Abdominal Aortic Aneurysm Segmentation from CT Images using Modified 3D U-Net with Deep Supervision

Habijan, Marija; Galic, Irena; Leventic, Hrvoje; Romic, Kresimir; Babin, Danilo
Abdominal Aortic Aneurysm Segmentation from CT Images using Modified 3D U-Net with Deep Supervision // 2020 International Symposium ELMAR
Zadar: Institute of Electrical and Electronics Engineers (IEEE), 2020. str. 123-128 doi:10.1109/elmar49956.2020.9219015 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)

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Naslov
Abdominal Aortic Aneurysm Segmentation from CT Images using Modified 3D U-Net with Deep Supervision

Autori
Habijan, Marija ; Galic, Irena ; Leventic, Hrvoje ; Romic, Kresimir ; Babin, Danilo

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
2020 International Symposium ELMAR / - Zadar : Institute of Electrical and Electronics Engineers (IEEE), 2020, 123-128

ISBN
978-1-7281-5973-7

Skup
62nd International Symposium ELMAR-2020

Mjesto i datum
Zadar, Hrvatska, 14.09.2020. - 15.09.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Abdominal aortic aneurysm, CT, Deep learning, Medical image segmentation, 3D U-Net

Sažetak
An abdominal aortic aneurysm (AAA) is a dangerous cardiovascular disease that can cause serious health complications and death. Methods that can provide automatic and accurate segmentation of the AAA can significantly help in preoperative planning and postoperative follow-ups. Therefore, in this work, we present an automatic method for AAA segmentation from CT images using a modified 3D U-Net network with deep supervision. We compare obtained results for AAA segmentation using original 3D U-Net, and modified 3D U-Net with deep supervision. The trained network is evaluated on 19 volumetric CT images from the publicly available dataset provided by the University Hospitals Leuven, Belgium, using four-fold cross-validation. We obtained DSC of 91.03% using modified 3D U-Net with deep supervision. Additionally, we provide a discussion of the effects of using up-sampling versus deconvolution layers and its influence on the performance of both networks for this specific clinical application.

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

Profili:

Avatar Url Hrvoje Leventić (autor)

Avatar Url Marija Habijan (autor)

Avatar Url Krešimir Romić (autor)

Avatar Url Irena Galić (autor)

Poveznice na cjeloviti tekst rada:

doi

Citiraj ovu publikaciju:

Habijan, Marija; Galic, Irena; Leventic, Hrvoje; Romic, Kresimir; Babin, Danilo
Abdominal Aortic Aneurysm Segmentation from CT Images using Modified 3D U-Net with Deep Supervision // 2020 International Symposium ELMAR
Zadar: Institute of Electrical and Electronics Engineers (IEEE), 2020. str. 123-128 doi:10.1109/elmar49956.2020.9219015 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Habijan, M., Galic, I., Leventic, H., Romic, K. & Babin, D. (2020) Abdominal Aortic Aneurysm Segmentation from CT Images using Modified 3D U-Net with Deep Supervision. U: 2020 International Symposium ELMAR doi:10.1109/elmar49956.2020.9219015.
@article{article, author = {Habijan, Marija and Galic, Irena and Leventic, Hrvoje and Romic, Kresimir and Babin, Danilo}, year = {2020}, pages = {123-128}, DOI = {10.1109/elmar49956.2020.9219015}, keywords = {Abdominal aortic aneurysm, CT, Deep learning, Medical image segmentation, 3D U-Net}, doi = {10.1109/elmar49956.2020.9219015}, isbn = {978-1-7281-5973-7}, title = {Abdominal Aortic Aneurysm Segmentation from CT Images using Modified 3D U-Net with Deep Supervision}, keyword = {Abdominal aortic aneurysm, CT, Deep learning, Medical image segmentation, 3D U-Net}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Zadar, Hrvatska} }
@article{article, author = {Habijan, Marija and Galic, Irena and Leventic, Hrvoje and Romic, Kresimir and Babin, Danilo}, year = {2020}, pages = {123-128}, DOI = {10.1109/elmar49956.2020.9219015}, keywords = {Abdominal aortic aneurysm, CT, Deep learning, Medical image segmentation, 3D U-Net}, doi = {10.1109/elmar49956.2020.9219015}, isbn = {978-1-7281-5973-7}, title = {Abdominal Aortic Aneurysm Segmentation from CT Images using Modified 3D U-Net with Deep Supervision}, keyword = {Abdominal aortic aneurysm, CT, Deep learning, Medical image segmentation, 3D U-Net}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Zadar, Hrvatska} }

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