BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

140 related articles for article (PubMed ID: 38595327)

  • 1. Cascaded cross-attention transformers and convolutional neural networks for multi-organ segmentation in male pelvic computed tomography.
    Pemmaraju R; Kim G; Mekki L; Song DY; Lee J
    J Med Imaging (Bellingham); 2024 Mar; 11(2):024009. PubMed ID: 38595327
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Male pelvic multi-organ segmentation using token-based transformer Vnet.
    Pan S; Lei Y; Wang T; Wynne J; Chang CW; Roper J; Jani AB; Patel P; Bradley JD; Liu T; Yang X
    Phys Med Biol; 2022 Oct; 67(20):. PubMed ID: 36170872
    [No Abstract]   [Full Text] [Related]  

  • 3. Automatic multi-organ segmentation in computed tomography images using hierarchical convolutional neural network.
    Sultana S; Robinson A; Song DY; Lee J
    J Med Imaging (Bellingham); 2020 Sep; 7(5):055001. PubMed ID: 33102622
    [No Abstract]   [Full Text] [Related]  

  • 4. 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]  

  • 5. Fully automated multiorgan segmentation of female pelvic magnetic resonance images with coarse-to-fine convolutional neural network.
    Zabihollahy F; Viswanathan AN; Schmidt EJ; Morcos M; Lee J
    Med Phys; 2021 Nov; 48(11):7028-7042. PubMed ID: 34609756
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A new architecture combining convolutional and transformer-based networks for automatic 3D multi-organ segmentation on CT images.
    Li C; Bagher-Ebadian H; Sultan R; Elshaikh M; Movsas B; Zhu D; Chetty IJ
    Med Phys; 2023 Nov; 50(11):6990-7002. PubMed ID: 37738468
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Segmentation of male pelvic organs on computed tomography with a deep neural network fine-tuned by a level-set method.
    Almeida G; Figueira AR; Lencart J; Tavares JMRS
    Comput Biol Med; 2022 Jan; 140():105107. PubMed ID: 34872011
    [TBL] [Abstract][Full Text] [Related]  

  • 8. CNN-based hierarchical coarse-to-fine segmentation of pelvic CT images for prostate cancer radiotherapy.
    Sultana S; Robinson A; Song DY; Lee J
    Proc SPIE Int Soc Opt Eng; 2020 Feb; 11315():. PubMed ID: 32341620
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation.
    Wang S; He K; Nie D; Zhou S; Gao Y; Shen D
    Med Image Anal; 2019 May; 54():168-178. PubMed ID: 30928830
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Pelvic multi-organ segmentation on cone-beam CT for prostate adaptive radiotherapy.
    Fu Y; Lei Y; Wang T; Tian S; Patel P; Jani AB; Curran WJ; Liu T; Yang X
    Med Phys; 2020 Aug; 47(8):3415-3422. PubMed ID: 32323330
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. 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]  

  • 14. Male pelvic multi-organ segmentation on transrectal ultrasound using anchor-free mask CNN.
    Lei Y; Wang T; Roper J; Jani AB; Patel SA; Curran WJ; Patel P; Liu T; Yang X
    Med Phys; 2021 Jun; 48(6):3055-3064. PubMed ID: 33894057
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Development of in-house fully residual deep convolutional neural network-based segmentation software for the male pelvic CT.
    Hirashima H; Nakamura M; Baillehache P; Fujimoto Y; Nakagawa S; Saruya Y; Kabasawa T; Mizowaki T
    Radiat Oncol; 2021 Jul; 16(1):135. PubMed ID: 34294090
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Multiscale Local Enhancement Deep Convolutional Networks for the Automated 3D Segmentation of Gross Tumor Volumes in Nasopharyngeal Carcinoma: A Multi-Institutional Dataset Study.
    Yang G; Dai Z; Zhang Y; Zhu L; Tan J; Chen Z; Zhang B; Cai C; He Q; Li F; Wang X; Yang W
    Front Oncol; 2022; 12():827991. PubMed ID: 35387126
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Pelvic Organ Segmentation Using Distinctive Curve Guided Fully Convolutional Networks.
    He K; Cao X; Shi Y; Nie D; Gao Y; Shen D
    IEEE Trans Med Imaging; 2019 Feb; 38(2):585-595. PubMed ID: 30176583
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets.
    Hu P; Wu F; Peng J; Bao Y; Chen F; Kong D
    Int J Comput Assist Radiol Surg; 2017 Mar; 12(3):399-411. PubMed ID: 27885540
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Patient-specific transfer learning for auto-segmentation in adaptive 0.35 T MRgRT of prostate cancer: a bi-centric evaluation.
    Kawula M; Hadi I; Nierer L; Vagni M; Cusumano D; Boldrini L; Placidi L; Corradini S; Belka C; Landry G; Kurz C
    Med Phys; 2023 Mar; 50(3):1573-1585. PubMed ID: 36259384
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Male pelvic CT multi-organ segmentation using synthetic MRI-aided dual pyramid networks.
    Lei Y; Wang T; Tian S; Fu Y; Patel P; Jani AB; Curran WJ; Liu T; Yang X
    Phys Med Biol; 2021 Apr; 66(8):. PubMed ID: 33780918
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.