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Journal Abstract Search

539 related items for PubMed ID: 32690103

  • 1. 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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  • 4. 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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  • 5. Comparison of atlas-based and deep learning methods for organs at risk delineation on head-and-neck CT images using an automated treatment planning system.
    Costea M, Zlate A, Durand M, Baudier T, Grégoire V, Sarrut D, Biston MC.
    Radiother Oncol; 2022 Dec 15; 177():61-70. PubMed ID: 36328093
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  • 7. Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring.
    van Dijk LV, Van den Bosch L, Aljabar P, Peressutti D, Both S, J H M Steenbakkers R, Langendijk JA, Gooding MJ, Brouwer CL.
    Radiother Oncol; 2020 Jan 15; 142():115-123. PubMed ID: 31653573
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  • 14. Comparing different CT, PET and MRI multi-modality image combinations for deep learning-based head and neck tumor segmentation.
    Ren J, Eriksen JG, Nijkamp J, Korreman SS.
    Acta Oncol; 2021 Nov 15; 60(11):1399-1406. PubMed ID: 34264157
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  • 16. 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 15; 51(7):4767-4777. PubMed ID: 38376454
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  • 17. Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models.
    Urago Y, Okamoto H, Kaneda T, Murakami N, Kashihara T, Takemori M, Nakayama H, Iijima K, Chiba T, Kuwahara J, Katsuta S, Nakamura S, Chang W, Saitoh H, Igaki H.
    Radiat Oncol; 2021 Sep 09; 16(1):175. PubMed ID: 34503533
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  • 19. 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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  • 20. Custom-Trained Deep Learning-Based Auto-Segmentation for Male Pelvic Iterative CBCT on C-Arm Linear Accelerators.
    Tegtmeier RC, Kutyreff CJ, Smetanick JL, Hobbis D, Laughlin BS, Toesca DAS, Clouser EL, Rong Y.
    Pract Radiat Oncol; 2024 Feb 25; 14(5):e383-e394. PubMed ID: 38325548
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