Comprehensive deep learning-based framework for automatic organs-at-risk segmentation in head-and-neck and pelvis for MR-guided radiation therapy planning

Czipczer Vanda and Kolozsvári Bernadett and Deák-Karancsi Borbála and Capala E. Marta and Pearson Rachel A. and Borzási Emőke and Együd Zsófia and Gaál Szilvia and Kelemen Gyöngyi and Kószó Renáta Lilla and Paczona Viktor Róbert and Végváry Zoltán and Karancsi Zsófia and Kékesi Ádám and Czunyi Edina and Irmai H. Blanka and Keresnyei G. Nóra and Nagypál Petra and Czabány Renáta and Gyalai Bence and Tass P. Bulcsú and Cziria Balázs and Cozzini Cristina and Estkowsky Lloyd and Ferenczi Lehel and Frontó András and Maxwell Ross and Megyeri István and Mian Michael and Tan Tao and Hideghéty Katalin and Ruskó László and et al.: Comprehensive deep learning-based framework for automatic organs-at-risk segmentation in head-and-neck and pelvis for MR-guided radiation therapy planning.
FRONTIERS IN PHYSICS, 11. ISSN 2296-424X (2023)

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Item Type: Journal Article
Szerzők száma: 36
Journal or Publication Title: FRONTIERS IN PHYSICS
Date: 2023
Volume: 11
Number of Pages: 17
Publication identifier: 1236792
ISSN: 2296-424X
Faculty/Unit: Albert Szent-Györgyi Medical School
Institution: Szegedi Tudományegyetem
Language: English
MTMT rekordazonosító: 34145498
DOI azonosító: https://doi.org/10.3389/fphy.2023.1236792
Date Deposited: 2024. Jan. 19. 10:29
Last Modified: 2024. Jan. 19. 21:41
URI: http://publicatio.bibl.u-szeged.hu/id/eprint/29344

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