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342 related items for PubMed ID: 30450825
21. 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 23; 16(1):113. PubMed ID: 34162410 [Abstract] [Full Text] [Related]
22. A convolutional neural network algorithm for automatic segmentation of head and neck organs at risk using deep lifelong learning. Chan JW, Kearney V, Haaf S, Wu S, Bogdanov M, Reddick M, Dixit N, Sudhyadhom A, Chen J, Yom SS, Solberg TD. Med Phys; 2019 May 23; 46(5):2204-2213. PubMed ID: 30887523 [Abstract] [Full Text] [Related]
23. Lung tumor segmentation in 4D CT images using motion convolutional neural networks. Momin S, Lei Y, Tian Z, Wang T, Roper J, Kesarwala AH, Higgins K, Bradley JD, Liu T, Yang X. Med Phys; 2021 Nov 23; 48(11):7141-7153. PubMed ID: 34469001 [Abstract] [Full Text] [Related]
26. 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 23; 136():106261. PubMed ID: 36446186 [Abstract] [Full Text] [Related]
30. Synthetic CT-aided multiorgan segmentation for CBCT-guided adaptive pancreatic radiotherapy. Dai X, Lei Y, Wynne J, Janopaul-Naylor J, Wang T, Roper J, Curran WJ, Liu T, Patel P, Yang X. Med Phys; 2021 Nov 23; 48(11):7063-7073. PubMed ID: 34609745 [Abstract] [Full Text] [Related]
31. 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 Nov 23; 11():638197. PubMed ID: 34026615 [Abstract] [Full Text] [Related]
32. Machine-assisted interpolation algorithm for semi-automated segmentation of highly deformable organs. Luximon DC, Abdulkadir Y, Chow PE, Morris ED, Lamb JM. Med Phys; 2022 Jan 23; 49(1):41-51. PubMed ID: 34783027 [Abstract] [Full Text] [Related]
37. [Automatic segmentation of head and neck organs at risk based on three-dimensional U-NET deep convolutional neural network]. Dai X, Wang X, Du L, Ma N, Xu S, Cai B, Wang S, Wang Z, Qu B. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2020 Feb 25; 37(1):136-141. PubMed ID: 32096387 [Abstract] [Full Text] [Related]
38. Automatic delineation of organ at risk in cervical cancer radiotherapy based on ensemble learning. Cheng T, Zhang Z, Yang X, Lu S, Qian D, Wang X, Zhu H. Zhong Nan Da Xue Xue Bao Yi Xue Ban; 2022 Aug 28; 47(8):1058-1064. PubMed ID: 36097773 [Abstract] [Full Text] [Related]
39. A study of positioning orientation effect on segmentation accuracy using convolutional neural networks for rectal cancer. Men K, Boimel P, Janopaul-Naylor J, Cheng C, Zhong H, Huang M, Geng H, Fan Y, Plastaras JP, Ben-Josef E, Xiao Y. J Appl Clin Med Phys; 2019 Jan 28; 20(1):110-117. PubMed ID: 30418701 [Abstract] [Full Text] [Related]
40. Cascaded deep learning-based auto-segmentation for head and neck cancer patients: Organs at risk on T2-weighted magnetic resonance imaging. Korte JC, Hardcastle N, Ng SP, Clark B, Kron T, Jackson P. Med Phys; 2021 Dec 28; 48(12):7757-7772. PubMed ID: 34676555 [Abstract] [Full Text] [Related] Page: [Previous] [Next] [New Search]