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Journal Abstract Search

430 related items for PubMed ID: 24784389

  • 1. 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
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  • 3. Clinical validation of atlas-based auto-segmentation of multiple target volumes and normal tissue (swallowing/mastication) structures in the head and neck.
    Teguh DN, Levendag PC, Voet PW, Al-Mamgani A, Han X, Wolf TK, Hibbard LS, Nowak P, Akhiat H, Dirkx ML, Heijmen BJ, Hoogeman MS.
    Int J Radiat Oncol Biol Phys; 2011 Nov 15; 81(4):950-7. PubMed ID: 20932664
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  • 4. Validation of clinical acceptability of an atlas-based segmentation algorithm for the delineation of organs at risk in head and neck cancer.
    Hoang Duc AK, Eminowicz G, Mendes R, Wong SL, McClelland J, Modat M, Cardoso MJ, Mendelson AF, Veiga C, Kadir T, D'Souza D, Ourselin S.
    Med Phys; 2015 Sep 15; 42(9):5027-34. PubMed ID: 26328953
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  • 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 15; 45(10):4558-4567. PubMed ID: 30136285
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  • 9. Comprehensive evaluation of ten deformable image registration algorithms for contour propagation between CT and cone-beam CT images in adaptive head & neck radiotherapy.
    Li X, Zhang Y, Shi Y, Wu S, Xiao Y, Gu X, Zhen X, Zhou L.
    PLoS One; 2017 Oct 15; 12(4):e0175906. PubMed ID: 28414799
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  • 10. 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 15; 46(2):576-589. PubMed ID: 30480818
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  • 13. Tissue segmentation of head and neck CT images for treatment planning: a multiatlas approach combined with intensity modeling.
    Fortunati V, Verhaart RF, van der Lijn F, Niessen WJ, Veenland JF, Paulides MM, van Walsum T.
    Med Phys; 2013 Jul 15; 40(7):071905. PubMed ID: 23822442
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  • 16. 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 20; 15(1):176. PubMed ID: 32690103
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  • 19. 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 20; 48(11):7052-7062. PubMed ID: 34655077
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