187 related articles for article (PubMed ID: 33831446)
1. Deep learning-based auto-segmentation of organs at risk in high-dose rate brachytherapy of cervical cancer.
Mohammadi R; Shokatian I; Salehi M; Arabi H; Shiri I; Zaidi H
Radiother Oncol; 2021 Jun; 159():231-240. PubMed ID: 33831446
[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 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]
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. Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network.
Liu Z; Liu X; Xiao B; Wang S; Miao Z; Sun Y; Zhang F
Phys Med; 2020 Jan; 69():184-191. PubMed ID: 31918371
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. 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]
9. 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]
10. Evaluation of deep learning-based auto-segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients.
Wang Z; Chang Y; Peng Z; Lv Y; Shi W; Wang F; Pei X; Xu XG
J Appl Clin Med Phys; 2020 Dec; 21(12):272-279. PubMed ID: 33238060
[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. Development and validation of a deep learning algorithm for auto-delineation of clinical target volume and organs at risk in cervical cancer radiotherapy.
Liu Z; Liu X; Guan H; Zhen H; Sun Y; Chen Q; Chen Y; Wang S; Qiu J
Radiother Oncol; 2020 Dec; 153():172-179. PubMed ID: 33039424
[TBL] [Abstract][Full Text] [Related]
13. MR to ultrasound image registration with segmentation-based learning for HDR prostate brachytherapy.
Chen Y; Xing L; Yu L; Liu W; Pooya Fahimian B; Niedermayr T; Bagshaw HP; Buyyounouski M; Han B
Med Phys; 2021 Jun; 48(6):3074-3083. PubMed ID: 33905566
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Evaluation of an MR-only interstitial gynecologic brachytherapy workflow using MR-line marker for catheter reconstruction.
Shaaer A; Paudel M; Smith M; Tonolete F; Nicolae A; Leung E; Ravi A
Brachytherapy; 2020; 19(5):642-650. PubMed ID: 32712027
[TBL] [Abstract][Full Text] [Related]
16. Radiobiological optimization comparison between pulse-dose-rate and high-dose-rate brachytherapy in patients with locally advanced cervical cancer.
Annede P; Dumas I; Schernberg A; Tailleur A; Fumagalli I; Bockel S; Mignot F; Kissel M; Deutsch E; Haie-Meder C; Chargari C
Brachytherapy; 2019; 18(3):370-377. PubMed ID: 30797698
[TBL] [Abstract][Full Text] [Related]
17. RefineNet-based automatic delineation of the clinical target volume and organs at risk for three-dimensional brachytherapy for cervical cancer.
Jiang X; Wang F; Chen Y; Yan S
Ann Transl Med; 2021 Dec; 9(23):1721. PubMed ID: 35071415
[TBL] [Abstract][Full Text] [Related]
18. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks.
Men K; Dai J; Li Y
Med Phys; 2017 Dec; 44(12):6377-6389. PubMed ID: 28963779
[TBL] [Abstract][Full Text] [Related]
19. Machine Segmentation of Pelvic Anatomy in MRI-Assisted Radiosurgery (MARS) for Prostate Cancer Brachytherapy.
Sanders JW; Lewis GD; Thames HD; Kudchadker RJ; Venkatesan AM; Bruno TL; Ma J; Pagel MD; Frank SJ
Int J Radiat Oncol Biol Phys; 2020 Dec; 108(5):1292-1303. PubMed ID: 32634543
[TBL] [Abstract][Full Text] [Related]
20. Deep Learning-based Non-rigid Image Registration for High-dose Rate Brachytherapy in Inter-fraction Cervical Cancer.
Salehi M; Vafaei Sadr A; Mahdavi SR; Arabi H; Shiri I; Reiazi R
J Digit Imaging; 2023 Apr; 36(2):574-587. PubMed ID: 36417026
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
