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Journal Abstract Search

337 related items for PubMed ID: 35263027

  • 1. Deep learning-based auto segmentation using generative adversarial network on magnetic resonance images obtained for head and neck cancer patients.
    Kawahara D, Tsuneda M, Ozawa S, Okamoto H, Nakamura M, Nishio T, Nagata Y.
    J Appl Clin Med Phys; 2022 May; 23(5):e13579. PubMed ID: 35263027
    [Abstract] [Full Text] [Related]

  • 2. 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; 46(6):2669-2682. PubMed ID: 31002188
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  • 3. 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; 48(12):7757-7772. PubMed ID: 34676555
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  • 4. Transfer learning for auto-segmentation of 17 organs-at-risk in the head and neck: Bridging the gap between institutional and public datasets.
    Clark B, Hardcastle N, Johnston LA, Korte J.
    Med Phys; 2024 Jul; 51(7):4767-4777. PubMed ID: 38376454
    [Abstract] [Full Text] [Related]

  • 5. AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy.
    Zhu W, Huang Y, Zeng L, Chen X, Liu Y, Qian Z, Du N, Fan W, Xie X.
    Med Phys; 2019 Feb; 46(2):576-589. PubMed ID: 30480818
    [Abstract] [Full Text] [Related]

  • 6. Automated delineation of head and neck organs at risk using synthetic MRI-aided mask scoring regional convolutional neural network.
    Dai X, Lei Y, Wang T, Zhou J, Roper J, McDonald M, Beitler JJ, Curran WJ, Liu T, Yang X.
    Med Phys; 2021 Oct; 48(10):5862-5873. PubMed ID: 34342878
    [Abstract] [Full Text] [Related]

  • 7. 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
    [Abstract] [Full Text] [Related]

  • 8. Auto-segmentation of organs at risk for head and neck radiotherapy planning: From atlas-based to deep learning methods.
    Vrtovec T, Močnik D, Strojan P, Pernuš F, Ibragimov B.
    Med Phys; 2020 Sep; 47(9):e929-e950. PubMed ID: 32510603
    [Abstract] [Full Text] [Related]

  • 9. Automatic multiorgan segmentation in thorax CT images using U-net-GAN.
    Dong X, Lei Y, Wang T, Thomas M, Tang L, Curran WJ, Liu T, Yang X.
    Med Phys; 2019 May; 46(5):2157-2168. PubMed ID: 30810231
    [Abstract] [Full Text] [Related]

  • 10. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.
    Ibragimov B, Xing L.
    Med Phys; 2017 Feb; 44(2):547-557. PubMed ID: 28205307
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  • 13. Weaving attention U-net: A novel hybrid CNN and attention-based method for organs-at-risk segmentation in head and neck CT images.
    Zhang Z, Zhao T, Gay H, Zhang W, Sun B.
    Med Phys; 2021 Nov; 48(11):7052-7062. PubMed ID: 34655077
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  • 15. 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 11; 66(4):045021. PubMed ID: 33412527
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  • 16. An evaluation of MR based deep learning auto-contouring for planning head and neck radiotherapy.
    Hague C, McPartlin A, Lee LW, Hughes C, Mullan D, Beasley W, Green A, Price G, Whitehurst P, Slevin N, van Herk M, West C, Chuter R.
    Radiother Oncol; 2021 May 11; 158():112-117. PubMed ID: 33636229
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