238 related articles for article (PubMed ID: 37597126)
41. Improved accuracy of auto-segmentation of organs at risk in radiotherapy planning for nasopharyngeal carcinoma based on fully convolutional neural network deep learning.
Peng Y; Liu Y; Shen G; Chen Z; Chen M; Miao J; Zhao C; Deng J; Qi Z; Deng X
Oral Oncol; 2023 Jan; 136():106261. PubMed ID: 36446186
[TBL] [Abstract][Full Text] [Related]
42. Atlas-based auto-segmentation for postoperative radiotherapy planning in endometrial and cervical cancers.
Kim N; Chang JS; Kim YB; Kim JS
Radiat Oncol; 2020 May; 15(1):106. PubMed ID: 32404123
[TBL] [Abstract][Full Text] [Related]
43. Towards deep-learning (DL) based fully automated target delineation for rectal cancer neoadjuvant radiotherapy using a divide-and-conquer strategy: a study with multicenter blind and randomized validation.
Geng J; Zhu X; Liu Z; Chen Q; Bai L; Wang S; Li Y; Wu H; Yue H; Du Y
Radiat Oncol; 2023 Oct; 18(1):164. PubMed ID: 37803462
[TBL] [Abstract][Full Text] [Related]
44. Gross tumor volume segmentation for head and neck cancer radiotherapy using deep dense multi-modality network.
Guo Z; Guo N; Gong K; Zhong S; Li Q
Phys Med Biol; 2019 Oct; 64(20):205015. PubMed ID: 31514173
[TBL] [Abstract][Full Text] [Related]
45. DeepTarget: Gross tumor and clinical target volume segmentation in esophageal cancer radiotherapy.
Jin D; Guo D; Ho TY; Harrison AP; Xiao J; Tseng CK; Lu L
Med Image Anal; 2021 Feb; 68():101909. PubMed ID: 33341494
[TBL] [Abstract][Full Text] [Related]
46. Geometric and Dosimetric Evaluation of Deep Learning-Based Automatic Delineation on CBCT-Synthesized CT and Planning CT for Breast Cancer Adaptive Radiotherapy: A Multi-Institutional Study.
Dai Z; Zhang Y; Zhu L; Tan J; Yang G; Zhang B; Cai C; Jin H; Meng H; Tan X; Jian W; Yang W; Wang X
Front Oncol; 2021; 11():725507. PubMed ID: 34858813
[TBL] [Abstract][Full Text] [Related]
47. A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy.
Chen X; Sun S; Bai N; Han K; Liu Q; Yao S; Tang H; Zhang C; Lu Z; Huang Q; Zhao G; Xu Y; Chen T; Xie X; Liu Y
Radiother Oncol; 2021 Jul; 160():175-184. PubMed ID: 33961914
[TBL] [Abstract][Full Text] [Related]
48. 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]
49. Comprehensive clinical evaluation of deep learning-based auto-segmentation for radiotherapy in patients with cervical cancer.
Chung SY; Chang JS; Kim YB
Front Oncol; 2023; 13():1119008. PubMed ID: 37188180
[TBL] [Abstract][Full Text] [Related]
50. Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images.
Chen W; Li Y; Dyer BA; Feng X; Rao S; Benedict SH; Chen Q; Rong Y
Radiat Oncol; 2020 Jul; 15(1):176. PubMed ID: 32690103
[TBL] [Abstract][Full Text] [Related]
51. 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]
52. 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]
53. Clinical Target Volume Auto-Segmentation of Esophageal Cancer for Radiotherapy After Radical Surgery Based on Deep Learning.
Cao R; Pei X; Ge N; Zheng C
Technol Cancer Res Treat; 2021; 20():15330338211034284. PubMed ID: 34387104
[TBL] [Abstract][Full Text] [Related]
54. Deep learning based automatic segmentation of organs-at-risk for 0.35 T MRgRT of lung tumors.
Ribeiro MF; Marschner S; Kawula M; Rabe M; Corradini S; Belka C; Riboldi M; Landry G; Kurz C
Radiat Oncol; 2023 Aug; 18(1):135. PubMed ID: 37574549
[TBL] [Abstract][Full Text] [Related]
55. Clinical evaluation on automatic segmentation results of convolutional neural networks in rectal cancer radiotherapy.
Li J; Song Y; Wu Y; Liang L; Li G; Bai S
Front Oncol; 2023; 13():1158315. PubMed ID: 37731629
[TBL] [Abstract][Full Text] [Related]
56. A prior-information-based automatic segmentation method for the clinical target volume in adaptive radiotherapy of cervical cancer.
Wang X; Chang Y; Pei X; Xu XG
J Appl Clin Med Phys; 2024 May; 25(5):e14350. PubMed ID: 38546277
[TBL] [Abstract][Full Text] [Related]
57. Magnetic resonance imaging-based target volume delineation in radiation therapy treatment planning for brain tumors using localized region-based active contour.
Aslian H; Sadeghi M; Mahdavi SR; Babapour Mofrad F; Astarakee M; Khaledi N; Fadavi P
Int J Radiat Oncol Biol Phys; 2013 Sep; 87(1):195-201. PubMed ID: 23920396
[TBL] [Abstract][Full Text] [Related]
58. Generating High-Quality Lymph Node Clinical Target Volumes for Head and Neck Cancer Radiation Therapy Using a Fully Automated Deep Learning-Based Approach.
Cardenas CE; Beadle BM; Garden AS; Skinner HD; Yang J; Rhee DJ; McCarroll RE; Netherton TJ; Gay SS; Zhang L; Court LE
Int J Radiat Oncol Biol Phys; 2021 Mar; 109(3):801-812. PubMed ID: 33068690
[TBL] [Abstract][Full Text] [Related]
59. 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]
60. Incremental retraining, clinical implementation, and acceptance rate of deep learning auto-segmentation for male pelvis in a multiuser environment.
Duan J; Vargas CE; Yu NY; Laughlin BS; Toesca DS; Keole S; Rwigema JCM; Wong WW; Schild SE; Feng X; Chen Q; Rong Y
Med Phys; 2023 Jul; 50(7):4079-4091. PubMed ID: 37287322
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
