Adaptable volumetric liver segmentation model for CT images using region-based features and convolutional neural network

Autor: Czipczer, Vanda, Manno-Kovacs, Andrea

Publikováno v: In Neurocomputing 21 September 2022 505:388-401

Deep-Learning-based Segmentation of Organs-at-Risk in the Head for MR-assisted Radiation Therapy Planning

Autor: Ruskó, László, Capala, Marta E., Czipczer, Vanda, Kolozsvári, Bernadett, Deák-Karancsi, Borbála, Czabány, Renáta, Gyalai, Bence, Tan, Tao, Végváry, Zoltán, Borzasi, Emőke, Együd, Zsófia, Kószó, Renáta, Paczona, Viktor, Fodor, Emese, Bobb, Chad, Cozzini, Cristina, Kaushik, Sandeep, Darázs, Barbara, Verduijn, Gerda M., Pearson, Rachel, Maxwell, Ross, McCallum, Hazel, Hernandez Tamames, Juan A., Hideghéty, Katalin, Petit, Steven F., Wiesinger, Florian, Douplik, Alexandre, Fred, Ana, Gamboa, Hugo

Publikováno v: BIOIMAGING
Borbála Deák-Karancsi
BIOIMAGING 2021-8th International Conference on Bioimaging; Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021, 31-43
STARTPAGE=31;ENDPAGE=43;TITLE=BIOIMAGING 2021-8th International Conference on Bioimaging; Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021

Segmentation of organs-at-risk (OAR) in MR images has several clinical applications; including radiation therapy (RT) planning. This paper presents a deep-learning-based method to segment 15 structures in the head region. The proposed method first ap

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ce5eacb4f20b9a5d9dc2353c3af6f19
https://doi.org/10.5220/0010235000310043

Segmentation of Multiple Organs in Computed Tomography and Magnetic Resonance Imaging Measurements

Autor: Kriston, András, Czipczer, Vanda, Manno-Kovács, Andrea, Kovács, Levente Attila, Benedek, Csaba, Szirányi, Tamás

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______2764::3e3fc2baf829ccc5135ea8db990dcb2f
https://eprints.sztaki.hu/9622/