BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

226 related articles for article (PubMed ID: 32418343)

  • 1. Head and neck cancer patient images for determining auto-segmentation accuracy in T2-weighted magnetic resonance imaging through expert manual segmentations.
    Cardenas CE; Mohamed ASR; Yang J; Gooding M; Veeraraghavan H; Kalpathy-Cramer J; Ng SP; Ding Y; Wang J; Lai SY; Fuller CD; Sharp G
    Med Phys; 2020 Jun; 47(5):2317-2322. PubMed ID: 32418343
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 3. CT images with expert manual contours of thoracic cancer for benchmarking auto-segmentation accuracy.
    Yang J; Veeraraghavan H; van Elmpt W; Dekker A; Gooding M; Sharp G
    Med Phys; 2020 Jul; 47(7):3250-3255. PubMed ID: 32128809
    [TBL] [Abstract][Full Text] [Related]  

  • 4. HaN-Seg: The head and neck organ-at-risk CT and MR segmentation dataset.
    Podobnik G; Strojan P; Peterlin P; Ibragimov B; Vrtovec T
    Med Phys; 2023 Mar; 50(3):1917-1927. PubMed ID: 36594372
    [TBL] [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
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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
    [TBL] [Abstract][Full Text] [Related]  

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

  • 8. Quality assurance assessment of intra-acquisition diffusion-weighted and T2-weighted magnetic resonance imaging registration and contour propagation for head and neck cancer radiotherapy.
    Naser MA; Wahid KA; Ahmed S; Salama V; Dede C; Edwards BW; Lin R; McDonald B; Salzillo TC; He R; Ding Y; Abdelaal MA; Thill D; O'Connell N; Willcut V; Christodouleas JP; Lai SY; Fuller CD; Mohamed ASR
    Med Phys; 2023 Apr; 50(4):2089-2099. PubMed ID: 36519973
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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; 42(9):5027-34. PubMed ID: 26328953
    [TBL] [Abstract][Full Text] [Related]  

  • 10. U-net architecture with embedded Inception-ResNet-v2 image encoding modules for automatic segmentation of organs-at-risk in head and neck cancer radiation therapy based on computed tomography scans.
    Siciarz P; McCurdy B
    Phys Med Biol; 2022 Jun; 67(11):. PubMed ID: 35134792
    [No Abstract]   [Full Text] [Related]  

  • 11. Auto-segmentation of normal and target structures in head and neck CT images: a feature-driven model-based approach.
    Qazi AA; Pekar V; Kim J; Xie J; Breen SL; Jaffray DA
    Med Phys; 2011 Nov; 38(11):6160-70. PubMed ID: 22047381
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Longitudinal fan-beam computed tomography dataset for head-and-neck squamous cell carcinoma patients.
    Bejarano T; De Ornelas-Couto M; Mihaylov IB
    Med Phys; 2019 May; 46(5):2526-2537. PubMed ID: 30806479
    [TBL] [Abstract][Full Text] [Related]  

  • 13. The feasibility of atlas-based automatic segmentation of MRI for H&N radiotherapy planning.
    Wardman K; Prestwich RJ; Gooding MJ; Speight RJ
    J Appl Clin Med Phys; 2016 Jul; 17(4):146-154. PubMed ID: 27455480
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Synthetic head and neck and phantom images for determining deformable image registration accuracy in magnetic resonance imaging.
    Ger RB; Yang J; Ding Y; Jacobsen MC; Cardenas CE; Fuller CD; Howell RM; Li H; Stafford RJ; Zhou S; Court LE
    Med Phys; 2018 Jul; ():. PubMed ID: 30007075
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Evaluation of the accuracy of deformable image registration on MRI with a physical phantom.
    Wu RY; Liu AY; Yang J; Williamson TD; Wisdom PG; Bronk L; Gao S; Grosshan DR; Fuller DC; Gunn GB; Ronald Zhu X; Frank SJ
    J Appl Clin Med Phys; 2020 Jan; 21(1):166-173. PubMed ID: 31808307
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch-based three-dimensional convolutional neural network.
    Dinkla AM; Florkow MC; Maspero M; Savenije MHF; Zijlstra F; Doornaert PAH; van Stralen M; Philippens MEP; van den Berg CAT; Seevinck PR
    Med Phys; 2019 Sep; 46(9):4095-4104. PubMed ID: 31206701
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Head and neck multi-organ auto-segmentation on CT images aided by synthetic MRI.
    Liu Y; Lei Y; Fu Y; Wang T; Zhou J; Jiang X; McDonald M; Beitler JJ; Curran WJ; Liu T; Yang X
    Med Phys; 2020 Sep; 47(9):4294-4302. PubMed ID: 32648602
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Automatic segmentation of head and neck primary tumors on MRI using a multi-view CNN.
    Schouten JPE; Noteboom S; Martens RM; Mes SW; Leemans CR; de Graaf P; Steenwijk MD
    Cancer Imaging; 2022 Jan; 22(1):8. PubMed ID: 35033188
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 12.