259 related articles for article (PubMed ID: 37042449)
1. Self-configuring nnU-Net for automatic delineation of the organs at risk and target in high-dose rate cervical brachytherapy, a low/middle-income country's experience.
Duprez D; Trauernicht C; Simonds H; Williams O
J Appl Clin Med Phys; 2023 Aug; 24(8):e13988. PubMed ID: 37042449
[TBL] [Abstract][Full Text] [Related]
2. Evaluation of auto-segmentation for brachytherapy of postoperative cervical cancer using deep learning-based workflow.
Wang J; Chen Y; Tu Y; Xie H; Chen Y; Luo L; Zhou P; Tang Q
Phys Med Biol; 2023 Feb; 68(5):. PubMed ID: 36753762
[No Abstract] [Full Text] [Related]
3. Automatic segmentation of magnetic resonance images for high-dose-rate cervical cancer brachytherapy using deep learning.
Yoganathan SA; Paul SN; Paloor S; Torfeh T; Chandramouli SH; Hammoud R; Al-Hammadi N
Med Phys; 2022 Mar; 49(3):1571-1584. PubMed ID: 35094405
[TBL] [Abstract][Full Text] [Related]
4. Automatic end-to-end VMAT treatment planning for rectal cancers.
Huang K; Chung C; Ludmir EB; Zhang L; Owens CA; Vega JG; Duryea J; Zhao Y; Chen X; Fuentes D; Cardenas CE; Briere TM; Beddar S; Court LE; Das P
J Appl Clin Med Phys; 2024 Apr; 25(4):e14259. PubMed ID: 38317597
[TBL] [Abstract][Full Text] [Related]
5. A deep learning-based self-adapting ensemble method for segmentation in gynecological brachytherapy.
Li Z; Zhu Q; Zhang L; Yang X; Li Z; Fu J
Radiat Oncol; 2022 Sep; 17(1):152. PubMed ID: 36064571
[TBL] [Abstract][Full Text] [Related]
6. Deformable image registration-based contour propagation yields clinically acceptable plans for MRI-based cervical cancer brachytherapy planning.
Chapman CH; Polan D; Vineberg K; Jolly S; Maturen KE; Brock KK; Prisciandaro JI
Brachytherapy; 2018; 17(2):360-367. PubMed ID: 29331573
[TBL] [Abstract][Full Text] [Related]
7. Delineation of clinical target volume and organs at risk in cervical cancer radiotherapy by deep learning networks.
Tian M; Wang H; Liu X; Ye Y; Ouyang G; Shen Y; Li Z; Wang X; Wu S
Med Phys; 2023 Oct; 50(10):6354-6365. PubMed ID: 37246619
[TBL] [Abstract][Full Text] [Related]
8. Three-dimensional deep neural network for automatic delineation of cervical cancer in planning computed tomography images.
Ding Y; Chen Z; Wang Z; Wang X; Hu D; Ma P; Ma C; Wei W; Li X; Xue X; Wang X
J Appl Clin Med Phys; 2022 Apr; 23(4):e13566. PubMed ID: 35192243
[TBL] [Abstract][Full Text] [Related]
9. Automatic contouring system for cervical cancer using convolutional neural networks.
Rhee DJ; Jhingran A; Rigaud B; Netherton T; Cardenas CE; Zhang L; Vedam S; Kry S; Brock KK; Shaw W; O'Reilly F; Parkes J; Burger H; Fakie N; Trauernicht C; Simonds H; Court LE
Med Phys; 2020 Nov; 47(11):5648-5658. PubMed ID: 32964477
[TBL] [Abstract][Full Text] [Related]
10. Evaluation of auto-segmentation for EBRT planning structures using deep learning-based workflow on cervical cancer.
Wang J; Chen Y; Xie H; Luo L; Tang Q
Sci Rep; 2022 Aug; 12(1):13650. PubMed ID: 35953516
[TBL] [Abstract][Full Text] [Related]
11. A comprehensive evaluation of adaptive daily planning for cervical cancer HDR brachytherapy.
Meerschaert R; Nalichowski A; Burmeister J; Paul A; Miller S; Hu Z; Zhuang L
J Appl Clin Med Phys; 2016 Nov; 17(6):323-333. PubMed ID: 27929505
[TBL] [Abstract][Full Text] [Related]
12. A novel two-step optimization method for tandem and ovoid high-dose-rate brachytherapy treatment for locally advanced cervical cancer.
Sharma M; Fields EC; Todor DA
Brachytherapy; 2015; 14(5):613-8. PubMed ID: 26092424
[TBL] [Abstract][Full Text] [Related]
13. A knowledge-based organ dose prediction tool for brachytherapy treatment planning of patients with cervical cancer.
Yusufaly TI; Kallis K; Simon A; Mayadev J; Yashar CM; Einck JP; Mell LK; Brown D; Scanderbeg D; Hild SJ; Covele B; Moore KL; Meyers SM
Brachytherapy; 2020; 19(5):624-634. PubMed ID: 32513446
[TBL] [Abstract][Full Text] [Related]
14. Comparison of computed tomography and magnetic resonance imaging in cervical cancer brachytherapy target and normal tissue contouring.
Eskander RN; Scanderbeg D; Saenz CC; Brown M; Yashar C
Int J Gynecol Cancer; 2010 Jan; 20(1):47-53. PubMed ID: 20130502
[TBL] [Abstract][Full Text] [Related]
15. Dynamics of High Risk Clinical Target Volume reduction during Brachytherapy and impact on its coverage in patients with inoperable cervical cancer.
Pobijakova M; Scepanovic D; Paluga M; Fekete M; Mardiak J
Neoplasma; 2018 Mar; 65(3):425-430. PubMed ID: 29788726
[TBL] [Abstract][Full Text] [Related]
16. Automatic segmentation and applicator reconstruction for CT-based brachytherapy of cervical cancer using 3D convolutional neural networks.
Zhang D; Yang Z; Jiang S; Zhou Z; Meng M; Wang W
J Appl Clin Med Phys; 2020 Oct; 21(10):158-169. PubMed ID: 32991783
[TBL] [Abstract][Full Text] [Related]
17. Prospects for daily online adaptive radiotherapy for cervical cancer: Auto-contouring evaluation and dosimetric outcomes.
Zhang Y; Wang G; Chang Y; Wang Z; Sun X; Sun Y; Zeng Z; Chen Y; Hu K; Qiu J; Yan J; Zhang F
Radiat Oncol; 2024 Jan; 19(1):6. PubMed ID: 38212767
[TBL] [Abstract][Full Text] [Related]
18. RapidBrachyDL: Rapid Radiation Dose Calculations in Brachytherapy Via Deep Learning.
Mao X; Pineau J; Keyes R; Enger SA
Int J Radiat Oncol Biol Phys; 2020 Nov; 108(3):802-812. PubMed ID: 32413546
[TBL] [Abstract][Full Text] [Related]
19. Practically achievable maximum high-risk clinical target volume doses in MRI-guided intracavitary brachytherapy for cervical cancer: a planning study.
Menon G; Huang F; Sloboda R; Pearcey R; Ghosh S
Brachytherapy; 2014; 13(6):572-8. PubMed ID: 25085455
[TBL] [Abstract][Full Text] [Related]
20. Automated applicator digitization for high-dose-rate cervix brachytherapy using image thresholding and density-based clustering.
Deufel CL; Tian S; Yan BB; Vaishnav BD; Haddock MG; Petersen IA
Brachytherapy; 2020; 19(1):111-118. PubMed ID: 31594729
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]