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Journal Abstract Search

520 related items for PubMed ID: 33961914

  • 1. A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy.
    Chen X, Sun S, Bai N, Han K, Liu Q, Yao S, Tang H, Zhang C, Lu Z, Huang Q, Zhao G, Xu Y, Chen T, Xie X, Liu Y.
    Radiother Oncol; 2021 Jul; 160():175-184. PubMed ID: 33961914
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  • 2. 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
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  • 3. 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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  • 7. Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery.
    Chung SY, Chang JS, Choi MS, Chang Y, Choi BS, Chun J, Keum KC, Kim JS, Kim YB.
    Radiat Oncol; 2021 Feb 25; 16(1):44. PubMed ID: 33632248
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  • 10. 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 25; 45(10):4558-4567. PubMed ID: 30136285
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  • 14. 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 25; 153():139-145. PubMed ID: 32991916
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  • 15. 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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  • 16. AAR-RT - A system for auto-contouring organs at risk on CT images for radiation therapy planning: Principles, design, and large-scale evaluation on head-and-neck and thoracic cancer cases.
    Wu X, Udupa JK, Tong Y, Odhner D, Pednekar GV, Simone CB, McLaughlin D, Apinorasethkul C, Apinorasethkul O, Lukens J, Mihailidis D, Shammo G, James P, Tiwari A, Wojtowicz L, Camaratta J, Torigian DA.
    Med Image Anal; 2019 May 25; 54():45-62. PubMed ID: 30831357
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  • 18. 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 25; 44(12):6377-6389. PubMed ID: 28963779
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  • 19. 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 25; 47(9):e929-e950. PubMed ID: 32510603
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  • 20. 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
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