BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

675 related articles for article (PubMed ID: 32097892)

  • 1. Self-channel-and-spatial-attention neural network for automated multi-organ segmentation on head and neck CT images.
    Gou S; Tong N; Qi S; Yang S; Chin R; Sheng K
    Phys Med Biol; 2020 Dec; 65(24):245034. PubMed ID: 32097892
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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
    [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. 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
    [TBL] [Abstract][Full Text] [Related]  

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

  • 8. Accurate and robust auto-segmentation of head and neck organ-at-risks based on a novel CNN fine-tuning workflow.
    Luan S; Wu K; Wu Y; Zhu B; Wei W; Xue X
    J Appl Clin Med Phys; 2024 Jan; 25(1):e14248. PubMed ID: 38128058
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Abdomen CT multi-organ segmentation using token-based MLP-Mixer.
    Pan S; Chang CW; Wang T; Wynne J; Hu M; Lei Y; Liu T; Patel P; Roper J; Yang X
    Med Phys; 2023 May; 50(5):3027-3038. PubMed ID: 36463516
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. 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]  

  • 12. ARPM-net: A novel CNN-based adversarial method with Markov random field enhancement for prostate and organs at risk segmentation in pelvic CT images.
    Zhang Z; Zhao T; Gay H; Zhang W; Sun B
    Med Phys; 2021 Jan; 48(1):227-237. PubMed ID: 33151620
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Multi-task edge-recalibrated network for male pelvic multi-organ segmentation on CT images.
    Tong N; Gou S; Chen S; Yao Y; Yang S; Cao M; Kishan A; Sheng K
    Phys Med Biol; 2021 Jan; 66(3):035001. PubMed ID: 33197901
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning.
    Liang S; Tang F; Huang X; Yang K; Zhong T; Hu R; Liu S; Yuan X; Zhang Y
    Eur Radiol; 2019 Apr; 29(4):1961-1967. PubMed ID: 30302589
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Self-paced DenseNet with boundary constraint for automated multi-organ segmentation on abdominal CT images.
    Tong N; Gou S; Niu T; Yang S; Sheng K
    Phys Med Biol; 2020 Jul; 65(13):135011. PubMed ID: 32657281
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Head and neck multi-organ segmentation on dual-energy CT using dual pyramid convolutional neural networks.
    Wang T; Lei Y; Roper J; Ghavidel B; Beitler JJ; McDonald M; Curran WJ; Liu T; Yang X
    Phys Med Biol; 2021 May; 66(11):. PubMed ID: 33915524
    [TBL] [Abstract][Full Text] [Related]  

  • 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; 44(12):6377-6389. PubMed ID: 28963779
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Improved accuracy of auto-segmentation of organs at risk in radiotherapy planning for nasopharyngeal carcinoma based on fully convolutional neural network deep learning.
    Peng Y; Liu Y; Shen G; Chen Z; Chen M; Miao J; Zhao C; Deng J; Qi Z; Deng X
    Oral Oncol; 2023 Jan; 136():106261. PubMed ID: 36446186
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 34.