Zobrazeno 1 - 10
of 938
pro vyhledávání: '"Auto-segmentation"'
Autor:
Yukari Nagayasu, Shoki Inui, Yoshihiro Ueda, Akira Masaoka, Masahide Tominaga, Masayoshi Miyazaki, Koji Konishi
Publikováno v:
Journal of Medical Physics, Vol 49, Iss 3, Pp 335-342 (2024)
Aims: This study aimed to evaluate the geometrical accuracy of atlas-based auto-segmentation (ABAS), deformable image registration (DIR), and deep learning auto-segmentation (DLAS) in adaptive radiotherapy (ART) for head-and-neck cancer (HNC). Subjec
Externí odkaz:
https://doaj.org/article/c079bb8921084ac89b27fffd84cd53c9
Autor:
Haibo Peng, Tao Liu, Pengcheng Li, Fang Yang, Xing Luo, Xiaoqing Sun, Dong Gao, Fengyu Lin, Lecheng Jia, Ningyue Xu, Huigang Tan, Xi Wang, Tao Ren
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract Radiotherapy has been demonstrated to be one of the most significant treatments for cervical cancer, during which accurate and efficient delineation of target volumes is critical. To alleviate the data demand of deep learning and promote the
Externí odkaz:
https://doaj.org/article/14432ac074454a4ebac3c2fcd455410c
Autor:
Sophia L. Bürkle, Dejan Kuhn, Tobias Fechter, Gianluca Radicioni, Nanna Hartong, Martin T. Freitag, Xuefeng Qiu, Efstratios Karagiannis, Anca-Ligia Grosu, Dimos Baltas, Constantinos Zamboglou, Simon K. B. Spohn
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-8 (2024)
Abstract This retrospective, multi-centered study aimed to improve high-quality radiation treatment (RT) planning workflows by training and testing a Convolutional Neural Network (CNN) to perform auto segmentations of organs at risk (OAR) for prostat
Externí odkaz:
https://doaj.org/article/29344628f458470ab9b902c7416d82cc
Autor:
Eva Meixner, Benjamin Glogauer, Sebastian Klüter, Friedrich Wagner, David Neugebauer, Line Hoeltgen, Lisa A. Dinges, Semi Harrabi, Jakob Liermann, Maria Vinsensia, Fabian Weykamp, Philipp Hoegen-Saßmannshausen, Jürgen Debus, Juliane Hörner-Rieber
Publikováno v:
Clinical and Translational Radiation Oncology, Vol 49, Iss , Pp 100855- (2024)
Introduction: Target volume delineation is routinely performed in postoperative radiotherapy (RT) for breast cancer patients, but it is a time-consuming process. The aim of the present study was to validate the quality, clinical usability and institu
Externí odkaz:
https://doaj.org/article/d1f874bc005e4865937b38bd9c3f3255
Autor:
Chavelli M. Kensen, Rita Simões, Anja Betgen, Lisa Wiersema, Doenja M.J. Lambregts, Femke P. Peters, Corrie A.M. Marijnen, Uulke A. van der Heide, Tomas M. Janssen
Publikováno v:
Physics and Imaging in Radiation Oncology, Vol 32, Iss , Pp 100648- (2024)
Background and purpose: In online adaptive magnetic resonance image (MRI)-guided radiotherapy (MRIgRT), manual contouring of rectal tumors on daily images is labor-intensive and time-consuming. Automation of this task is complex due to substantial va
Externí odkaz:
https://doaj.org/article/d0e84928864f49dba82a2e7cc5e0c148
Akademický článek
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Autor:
Lorenzo Radici, Cristina Piva, Valeria Casanova Borca, Domenico Cante, Silvia Ferrario, Marina Paolini, Laura Cabras, Edoardo Petrucci, Pierfrancesco Franco, Maria Rosa La Porta, Massimo Pasquino
Publikováno v:
Clinical and Translational Radiation Oncology, Vol 47, Iss , Pp 100796- (2024)
Purpose: Aim of the present study is to characterize a deep learning-based auto-segmentation software (DL) for prostate cone beam computed tomography (CBCT) images and to evaluate its applicability in clinical adaptive radiation therapy routine. Mate
Externí odkaz:
https://doaj.org/article/77224347dc7f4521851491a153542037
Publikováno v:
Acta Oncologica, Vol 63, Iss 1 (2024)
Background: Deep learning (DL) models for auto-segmentation in radiotherapy have been extensively studied in retrospective and pilot settings. However, these studies might not reflect the clinical setting. This study compares the use of a clinically
Externí odkaz:
https://doaj.org/article/965563cefa2e42b48f70b9bd97f92b6a
Autor:
Hanhui Huang, Yukun Shi, Qin Chen, Huiqing Xu, Sicong Song, Yujie Shi, Furao Shen, Junxuan Fan
Publikováno v:
Geoscience Data Journal, Vol 11, Iss 1, Pp 46-56 (2024)
Abstract Fusulinid foraminifera are among the most common microfossils of the Late Palaeozoic and act as key fossils for stratigraphic correlation, paleogeographic and paleoenvironmental indication, and evolutionary studies of marine life. Accurate a
Externí odkaz:
https://doaj.org/article/65932180cfcc44e6afb71dc52738f8e8
Autor:
Yiling Wang, Elia Lombardo, Lili Huang, Michele Avanzo, Giuseppe Fanetti, Giovanni Franchin, Sebastian Zschaeck, Julian Weingärtner, Claus Belka, Marco Riboldi, Christopher Kurz, Guillaume Landry
Publikováno v:
Radiation Oncology, Vol 19, Iss 1, Pp 1-13 (2024)
Abstract Objectives Deep learning-based auto-segmentation of head and neck cancer (HNC) tumors is expected to have better reproducibility than manual delineation. Positron emission tomography (PET) and computed tomography (CT) are commonly used in tu
Externí odkaz:
https://doaj.org/article/0b34ea12b3c6417591771b9c90ca0c0a