Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer
Author:
Funder
Eurostars
Geneva University Hospitals
Swiss National Science Foundation
Publisher
Elsevier BV
Subject
Radiology, Nuclear Medicine and imaging,Oncology,Hematology
Reference62 articles.
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3. Recommendations from Gynaecological (GYN) GEC-ESTRO Working Group (IV): Basic principles and parameters for MR imaging within the frame of image based adaptive cervix cancer brachytherapy;Dimopoulos;Radiother Oncol,2012
4. Recommendations from gynaecological (GYN) GEC ESTRO working group (II): concepts and terms in 3D image-based treatment planning in cervix cancer brachytherapy-3D dose volume parameters and aspects of 3D image-based anatomy, radiation physics, radiobiology;Pötter;Radiother Oncol,2006
5. Recommendations from Gynaecological (GYN) GEC-ESTRO Working Group: considerations and pitfalls in commissioning and applicator reconstruction in 3D image-based treatment planning of cervix cancer brachytherapy;Hellebust;Radiother Oncol,2010
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