These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
587 related articles for article (PubMed ID: 31653573)
41. A method for a priori estimation of best feasible DVH for organs-at-risk: Validation for head and neck VMAT planning. Ahmed S; Nelms B; Gintz D; Caudell J; Zhang G; Moros EG; Feygelman V Med Phys; 2017 Oct; 44(10):5486-5497. PubMed ID: 28777469 [TBL] [Abstract][Full Text] [Related]
42. A Preliminary Experience of Implementing Deep-Learning Based Auto-Segmentation in Head and Neck Cancer: A Study on Real-World Clinical Cases. Zhong Y; Yang Y; Fang Y; Wang J; Hu W Front Oncol; 2021; 11():638197. PubMed ID: 34026615 [TBL] [Abstract][Full Text] [Related]
43. Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis. Liu P; Sun Y; Zhao X; Yan Y Biomed Eng Online; 2023 Nov; 22(1):104. PubMed ID: 37915046 [TBL] [Abstract][Full Text] [Related]
44. Interobserver variability in organ at risk delineation in head and neck cancer. van der Veen J; Gulyban A; Willems S; Maes F; Nuyts S Radiat Oncol; 2021 Jun; 16(1):120. PubMed ID: 34183040 [TBL] [Abstract][Full Text] [Related]
45. Geometric evaluations of CT and MRI based deep learning segmentation for brain OARs in radiotherapy. Alzahrani N; Henry A; Clark A; Murray L; Nix M; Al-Qaisieh B Phys Med Biol; 2023 Aug; 68(17):. PubMed ID: 37579753 [No Abstract] [Full Text] [Related]
46. A comparative study of auto-contouring softwares in delineation of organs at risk in lung cancer and rectal cancer. Chen W; Wang C; Zhan W; Jia Y; Ruan F; Qiu L; Yang S; Li Y Sci Rep; 2021 Nov; 11(1):23002. PubMed ID: 34836989 [TBL] [Abstract][Full Text] [Related]
47. The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer. Guo H; Wang J; Xia X; Zhong Y; Peng J; Zhang Z; Hu W Radiat Oncol; 2021 Jun; 16(1):113. PubMed ID: 34162410 [TBL] [Abstract][Full Text] [Related]
48. 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]
49. Evaluating the Effectiveness of Deep Learning Contouring across Multiple Radiotherapy Centres. Walker Z; Bartley G; Hague C; Kelly D; Navarro C; Rogers J; South C; Temple S; Whitehurst P; Chuter R Phys Imaging Radiat Oncol; 2022 Oct; 24():121-128. PubMed ID: 36405563 [TBL] [Abstract][Full Text] [Related]
50. 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]
51. Use of auto-segmentation in the delineation of target volumes and organs at risk in head and neck. Lim JY; Leech M Acta Oncol; 2016 Jul; 55(7):799-806. PubMed ID: 27248772 [TBL] [Abstract][Full Text] [Related]
52. 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]
53. Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer. Choi MS; Choi BS; Chung SY; Kim N; Chun J; Kim YB; Chang JS; Kim JS Radiother Oncol; 2020 Dec; 153():139-145. PubMed ID: 32991916 [TBL] [Abstract][Full Text] [Related]
54. Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: a clinical validation. Daisne JF; Blumhofer A Radiat Oncol; 2013 Jun; 8():154. PubMed ID: 23803232 [TBL] [Abstract][Full Text] [Related]
55. Head-and-neck organs-at-risk auto-delineation using dual pyramid networks for CBCT-guided adaptive radiotherapy. Dai X; Lei Y; Wang T; Dhabaan AH; McDonald M; Beitler JJ; Curran WJ; Zhou J; Liu T; Yang X Phys Med Biol; 2021 Feb; 66(4):045021. PubMed ID: 33412527 [TBL] [Abstract][Full Text] [Related]
56. Automatic segmentation of head and neck CT images for radiotherapy treatment planning using multiple atlases, statistical appearance models, and geodesic active contours. Fritscher KD; Peroni M; Zaffino P; Spadea MF; Schubert R; Sharp G Med Phys; 2014 May; 41(5):051910. PubMed ID: 24784389 [TBL] [Abstract][Full Text] [Related]
58. A novel semi auto-segmentation method for accurate dose and NTCP evaluation in adaptive head and neck radiotherapy. Gan Y; Langendijk JA; Oldehinkel E; Scandurra D; Sijtsema NM; Lin Z; Both S; Brouwer CL Radiother Oncol; 2021 Nov; 164():167-174. PubMed ID: 34597740 [TBL] [Abstract][Full Text] [Related]
59. Evaluating automatically generated normal tissue contours for safe use in head and neck and cervical cancer treatment planning. Douglas R; Olanrewaju A; Mumme R; Zhang L; Beadle BM; Court LE J Appl Clin Med Phys; 2024 Jul; 25(7):e14338. PubMed ID: 38610118 [TBL] [Abstract][Full Text] [Related]
60. Gross failure rates and failure modes for a commercial AI-based auto-segmentation algorithm in head and neck cancer patients. Temple SWP; Rowbottom CG J Appl Clin Med Phys; 2024 Jun; 25(6):e14273. PubMed ID: 38263866 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]