149 related articles for article (PubMed ID: 34122573)
21. 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]
22. Adaptive image guided brachytherapy for cervical cancer: a combined MRI-/CT-planning technique with MRI only at first fraction.
Nesvacil N; Pötter R; Sturdza A; Hegazy N; Federico M; Kirisits C
Radiother Oncol; 2013 Apr; 107(1):75-81. PubMed ID: 23068712
[TBL] [Abstract][Full Text] [Related]
23. Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer.
Kawula M; Purice D; Li M; Vivar G; Ahmadi SA; Parodi K; Belka C; Landry G; Kurz C
Radiat Oncol; 2022 Jan; 17(1):21. PubMed ID: 35101068
[TBL] [Abstract][Full Text] [Related]
24. Generalizability of deep learning in organ-at-risk segmentation: A transfer learning study in cervical brachytherapy.
Ni R; Han K; Haibe-Kains B; Rink A
Radiother Oncol; 2024 May; 197():110332. PubMed ID: 38763356
[TBL] [Abstract][Full Text] [Related]
25. ARPM-net: A novel CNN-based adversarial method with Markov random field enhancement for prostate and organs at risk segmentation in pelvic CT images.
Zhang Z; Zhao T; Gay H; Zhang W; Sun B
Med Phys; 2021 Jan; 48(1):227-237. PubMed ID: 33151620
[TBL] [Abstract][Full Text] [Related]
26. Comparison of dose volume parameters evaluated using three forward planning - optimization techniques in cervical cancer brachytherapy involving two applicators.
Chakrabarti B; Basu-Roy S; Kar SK; Das S; Lahiri A
J Contemp Brachytherapy; 2017 Oct; 9(5):431-445. PubMed ID: 29204164
[TBL] [Abstract][Full Text] [Related]
27. The use of trans-applicator intracavitary ultrasonography in brachytherapy for cervical cancer: phantom study of a novel approach to 3D image-guided brachytherapy.
Tamaki T; Miyaura K; Murakami T; Kumazaki Y; Suzuki Y; Nakano T; Kato S
J Contemp Brachytherapy; 2017 Apr; 9(2):151-157. PubMed ID: 28533804
[TBL] [Abstract][Full Text] [Related]
28. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks.
Tong N; Gou S; Yang S; Ruan D; Sheng K
Med Phys; 2018 Oct; 45(10):4558-4567. PubMed ID: 30136285
[TBL] [Abstract][Full Text] [Related]
29. Deep learning-based two-step organs at risk auto-segmentation model for brachytherapy planning in parotid gland carcinoma.
Li ZY; Yue JH; Wang W; Wu WJ; Zhou FG; Zhang J; Liu B
J Contemp Brachytherapy; 2022 Dec; 14(6):527-535. PubMed ID: 36819465
[TBL] [Abstract][Full Text] [Related]
30. Learning-based automatic segmentation of arteriovenous malformations on contrast CT images in brain stereotactic radiosurgery.
Wang T; Lei Y; Tian S; Jiang X; Zhou J; Liu T; Dresser S; Curran WJ; Shu HK; Yang X
Med Phys; 2019 Jul; 46(7):3133-3141. PubMed ID: 31050804
[TBL] [Abstract][Full Text] [Related]
31. Localizing intracavitary brachytherapy applicators from cone-beam CT x-ray projections via a novel iterative forward projection matching algorithm.
Pokhrel D; Murphy MJ; Todor DA; Weiss E; Williamson JF
Med Phys; 2011 Feb; 38(2):1070-80. PubMed ID: 21452744
[TBL] [Abstract][Full Text] [Related]
32. Deep Learning using Pre-Brachytherapy MRI to Automatically Predict Applicator Induced Complex Uterine Deformation.
Ghosh S; Punithakumar K; Huang F; Menon G; Boulanger P
Annu Int Conf IEEE Eng Med Biol Soc; 2022 Jul; 2022():3826-3829. PubMed ID: 36086328
[TBL] [Abstract][Full Text] [Related]
33. Deep-learning assisted automatic digitization of interstitial needles in 3D CT image based high dose-rate brachytherapy of gynecological cancer.
Jung H; Shen C; Gonzalez Y; Albuquerque K; Jia X
Phys Med Biol; 2019 Oct; 64(21):215003. PubMed ID: 31470425
[TBL] [Abstract][Full Text] [Related]
34. Comparison of the Dosimetric Influence of Applicator Displacement on 2D and 3D Brachytherapy for Cervical Cancer Treatment.
Wu A; Tang D; Wu A; Liu Y; Qian L; Zhu L
Technol Cancer Res Treat; 2021; 20():15330338211041201. PubMed ID: 34569371
[TBL] [Abstract][Full Text] [Related]
35. Segmentation of Clinical Target Volume From CT Images for Cervical Cancer Using Deep Learning.
Huang M; Feng C; Sun D; Cui M; Zhao D
Technol Cancer Res Treat; 2023; 22():15330338221139164. PubMed ID: 36601655
[No Abstract] [Full Text] [Related]
36. Evaluation on Auto-segmentation of the Clinical Target Volume (CTV) for Graves' Ophthalmopathy (GO) with a Fully Convolutional Network (FCN) on CT Images.
Jiang J; Luo Y; Wang F; Fu Y; Yu H; He Y
Curr Med Imaging; 2021; 17(3):404-409. PubMed ID: 32914716
[TBL] [Abstract][Full Text] [Related]
37. Multi-needle Localization with Attention U-Net in US-guided HDR Prostate Brachytherapy.
Zhang Y; Lei Y; Qiu RLJ; Wang T; Wang H; Jani AB; Curran WJ; Patel P; Liu T; Yang X
Med Phys; 2020 Jul; 47(7):2735-2745. PubMed ID: 32155666
[TBL] [Abstract][Full Text] [Related]
38. Automatic clinical target volume delineation for cervical cancer in CT images using deep learning.
Shi J; Ding X; Liu X; Li Y; Liang W; Wu J
Med Phys; 2021 Jul; 48(7):3968-3981. PubMed ID: 33905545
[TBL] [Abstract][Full Text] [Related]
39. CT and MR image fusion of tandem and ring applicator using rigid registration in intracavitary brachytherapy planning.
Oinam AS; Tomar P; Patel FD; Singh L; Rai B; Bahl A
J Appl Clin Med Phys; 2014 Mar; 15(2):4206. PubMed ID: 24710430
[TBL] [Abstract][Full Text] [Related]
40. Development of prototype shielded cervical intracavitary brachytherapy applicators compatible with CT and MR imaging.
Price MJ; Jackson EF; Gifford KA; Eifel PJ; Mourtada F
Med Phys; 2009 Dec; 36(12):5515-24. PubMed ID: 20095264
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
