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Journal Abstract Search

450 related items for PubMed ID: 36446186

  • 1. 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
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  • 5. 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
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  • 6. 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
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  • 10. [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
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  • 11. Automatic segmentation for adaptive planning in nasopharyngeal carcinoma IMRT: Time, geometrical, and dosimetric analysis.
    Fung NTC, Hung WM, Sze CK, Lee MCH, Ng WT.
    Med Dosim; 2020 Feb 25; 45(1):60-65. PubMed ID: 31345672
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  • 12. [Not Available].
    Zhang L, Liu Z, Zhang L, Wu Z, Yu X, Holmes J, Feng H, Dai H, Li X, Li Q, Wong WW, Vora SA, Zhu D, Liu T, Liu W.
    Med Phys; 2024 Mar 25; 51(3):2187-2199. PubMed ID: 38319676
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  • 13. Shape constrained fully convolutional DenseNet with adversarial training for multiorgan segmentation on head and neck CT and low-field MR images.
    Tong N, Gou S, Yang S, Cao M, Sheng K.
    Med Phys; 2019 Jun 25; 46(6):2669-2682. PubMed ID: 31002188
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  • 14. Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer.
    Ahn SH, Yeo AU, Kim KH, Kim C, Goh Y, Cho S, Lee SB, Lim YK, Kim H, Shin D, Kim T, Kim TH, Youn SH, Oh ES, Jeong JH.
    Radiat Oncol; 2019 Nov 27; 14(1):213. PubMed ID: 31775825
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  • 15. Evaluation of a deep image-to-image network (DI2IN) auto-segmentation algorithm across a network of cancer centers.
    Rayn K, Gupta V, Mulinti S, Clark R, Magliari A, Chaudhari S, Garima G, Beriwal S.
    J Cancer Res Ther; 2024 Apr 01; 20(3):1020-1025. PubMed ID: 39023610
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  • 16. 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 01; 69():184-191. PubMed ID: 31918371
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  • 17. 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 Jan 01; 20():15330338211034284. PubMed ID: 34387104
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  • 18. Clinical Validation of a Deep-Learning Segmentation Software in Head and Neck: An Early Analysis in a Developing Radiation Oncology Center.
    D'Aviero A, Re A, Catucci F, Piccari D, Votta C, Piro D, Piras A, Di Dio C, Iezzi M, Preziosi F, Menna S, Quaranta F, Boschetti A, Marras M, Miccichè F, Gallus R, Indovina L, Bussu F, Valentini V, Cusumano D, Mattiucci GC.
    Int J Environ Res Public Health; 2022 Jul 25; 19(15):. PubMed ID: 35897425
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  • 19. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.
    Ibragimov B, Xing L.
    Med Phys; 2017 Feb 25; 44(2):547-557. PubMed ID: 28205307
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  • 20. Evaluating Automatic Segmentation for Swallowing-Related Organs for Head and Neck Cancer.
    Li Y, Rao S, Chen W, Azghadi SF, Nguyen KNB, Moran A, Usera BM, Dyer BA, Shang L, Chen Q, Rong Y.
    Technol Cancer Res Treat; 2022 Feb 25; 21():15330338221105724. PubMed ID: 35790457
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