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Journal Abstract Search

1173 related items for PubMed ID: 31729403

  • 1. Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks.
    Sandfort V, Yan K, Pickhardt PJ, Summers RM.
    Sci Rep; 2019 Nov 15; 9(1):16884. PubMed ID: 31729403
    [Abstract] [Full Text] [Related]

  • 2. Anatomy-constrained synthesis for spleen segmentation improvement in unpaired mouse micro-CT scans with 3D CycleGAN.
    Jiang L, Xu D, Sheng K.
    Biomed Phys Eng Express; 2024 Aug 08; 10(5):. PubMed ID: 39094603
    [Abstract] [Full Text] [Related]

  • 3. A conventional-to-spectral CT image translation augmentation workflow for robust contrast injection-independent organ segmentation.
    Lartaud PJ, Dupont C, Hallé D, Schleef A, Dessouky R, Vlachomitrou AS, Rouet JM, Nempont O, Boussel L.
    Med Phys; 2022 Feb 08; 49(2):1108-1122. PubMed ID: 34689353
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  • 4. CT2US: Cross-modal transfer learning for kidney segmentation in ultrasound images with synthesized data.
    Song Y, Zheng J, Lei L, Ni Z, Zhao B, Hu Y.
    Ultrasonics; 2022 May 08; 122():106706. PubMed ID: 35149255
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  • 5. Cross-modal Transfer Learning Based on an Improved CycleGAN Model for Accurate Kidney Segmentation in Ultrasound Images.
    Guo S, Chen H, Sheng X, Xiong Y, Wu M, Fischer K, Tasian GE, Fan Y, Yin S.
    Ultrasound Med Biol; 2024 Nov 08; 50(11):1638-1645. PubMed ID: 39181806
    [Abstract] [Full Text] [Related]

  • 6. Synthesis of CT images from digital body phantoms using CycleGAN.
    Russ T, Goerttler S, Schnurr AK, Bauer DF, Hatamikia S, Schad LR, Zöllner FG, Chung K.
    Int J Comput Assist Radiol Surg; 2019 Oct 08; 14(10):1741-1750. PubMed ID: 31378841
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  • 7. Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets.
    Jiang J, Hu YC, Tyagi N, Zhang P, Rimner A, Deasy JO, Veeraraghavan H.
    Med Phys; 2019 Oct 08; 46(10):4392-4404. PubMed ID: 31274206
    [Abstract] [Full Text] [Related]

  • 8. Unsupervised segmentation and quantification of COVID-19 lesions on computed Tomography scans using CycleGAN.
    Connell M, Xin Y, Gerard SE, Herrmann J, Shah PK, Martin KT, Rezoagli E, Ippolito D, Rajaei J, Baron R, Delvecchio P, Humayun S, Rizi RR, Bellani G, Cereda M.
    Methods; 2022 Sep 08; 205():200-209. PubMed ID: 35817338
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  • 11. Bone segmentation on whole-body CT using convolutional neural network with novel data augmentation techniques.
    Noguchi S, Nishio M, Yakami M, Nakagomi K, Togashi K.
    Comput Biol Med; 2020 Jun 08; 121():103767. PubMed ID: 32339097
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  • 15. Synthetic MRI-aided multi-organ segmentation on male pelvic CT using cycle consistent deep attention network.
    Dong X, Lei Y, Tian S, Wang T, Patel P, Curran WJ, Jani AB, Liu T, Yang X.
    Radiother Oncol; 2019 Dec 08; 141():192-199. PubMed ID: 31630868
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  • 16. Convolutional neural networks for skull-stripping in brain MR imaging using silver standard masks.
    Lucena O, Souza R, Rittner L, Frayne R, Lotufo R.
    Artif Intell Med; 2019 Jul 08; 98():48-58. PubMed ID: 31521252
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  • 17. 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 08; 48(1):227-237. PubMed ID: 33151620
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  • 18. Probabilistic self-learning framework for low-dose CT denoising.
    Bai T, Wang B, Nguyen D, Jiang S.
    Med Phys; 2021 May 08; 48(5):2258-2270. PubMed ID: 33621348
    [Abstract] [Full Text] [Related]

  • 19. Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients.
    Gutierrez A, Tuladhar A, Wilms M, Rajashekar D, Hill MD, Demchuk A, Goyal M, Fiehler J, Forkert ND.
    Int J Comput Assist Radiol Surg; 2023 May 08; 18(5):827-836. PubMed ID: 36607506
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