BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

1443 related articles for article (PubMed ID: 30302589)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 5. Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs.
    Tang F; Liang S; Zhong T; Huang X; Deng X; Zhang Y; Zhou L
    Eur Radiol; 2020 Feb; 30(2):823-832. PubMed ID: 31650265
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 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; 45(10):4558-4567. PubMed ID: 30136285
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network.
    Liu Z; Liu X; Xiao B; Wang S; Miao Z; Sun Y; Zhang F
    Phys Med; 2020 Jan; 69():184-191. PubMed ID: 31918371
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. Automatic multiorgan segmentation in thorax CT images using U-net-GAN.
    Dong X; Lei Y; Wang T; Thomas M; Tang L; Curran WJ; Liu T; Yang X
    Med Phys; 2019 May; 46(5):2157-2168. PubMed ID: 30810231
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Automatic segmentation of organs-at-risks of nasopharynx cancer and lung cancer by cross-layer attention fusion network with TELD-Loss.
    Liu Z; Sun C; Wang H; Li Z; Gao Y; Lei W; Zhang S; Wang G; Zhang S
    Med Phys; 2021 Nov; 48(11):6987-7002. PubMed ID: 34608652
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. 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; 16(1):113. PubMed ID: 34162410
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer.
    Ahn SH; Yeo AU; Kim KH; Kim C; Goh Y; Cho S; Lee SB; Lim YK; Kim H; Shin D; Kim T; Kim TH; Youn SH; Oh ES; Jeong JH
    Radiat Oncol; 2019 Nov; 14(1):213. PubMed ID: 31775825
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques.
    Zhu J; Zhang J; Qiu B; Liu Y; Liu X; Chen L
    Acta Oncol; 2019 Feb; 58(2):257-264. PubMed ID: 30398090
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Deep convolutional neural network for segmentation of thoracic organs-at-risk using cropped 3D images.
    Feng X; Qing K; Tustison NJ; Meyer CH; Chen Q
    Med Phys; 2019 May; 46(5):2169-2180. PubMed ID: 30830685
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Artificial Intelligence Radiotherapy Planning: Automatic Segmentation of Human Organs in CT Images Based on a Modified Convolutional Neural Network.
    Shen G; Jin X; Sun C; Li Q
    Front Public Health; 2022; 10():813135. PubMed ID: 35493368
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A convolutional neural network algorithm for automatic segmentation of head and neck organs at risk using deep lifelong learning.
    Chan JW; Kearney V; Haaf S; Wu S; Bogdanov M; Reddick M; Dixit N; Sudhyadhom A; Chen J; Yom SS; Solberg TD
    Med Phys; 2019 May; 46(5):2204-2213. PubMed ID: 30887523
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. 3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning.
    Daoud B; Morooka K; Kurazume R; Leila F; Mnejja W; Daoud J
    Comput Med Imaging Graph; 2019 Oct; 77():101644. PubMed ID: 31426004
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 73.