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Journal Abstract Search

1528 related items for PubMed ID: 30734932

  • 1. U-Net based deep learning bladder segmentation in CT urography.
    Ma X, Hadjiiski LM, Wei J, Chan HP, Cha KH, Cohan RH, Caoili EM, Samala R, Zhou C, Lu Y.
    Med Phys; 2019 Apr; 46(4):1752-1765. PubMed ID: 30734932
    [Abstract] [Full Text] [Related]

  • 2. Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets.
    Cha KH, Hadjiiski L, Samala RK, Chan HP, Caoili EM, Cohan RH.
    Med Phys; 2016 Apr; 43(4):1882. PubMed ID: 27036584
    [Abstract] [Full Text] [Related]

  • 3. Deep-learning convolutional neural network: Inner and outer bladder wall segmentation in CT urography.
    Gordon MN, Hadjiiski LM, Cha KH, Samala RK, Chan HP, Cohan RH, Caoili EM.
    Med Phys; 2019 Feb; 46(2):634-648. PubMed ID: 30520055
    [Abstract] [Full Text] [Related]

  • 4. Urinary bladder segmentation in CT urography (CTU) using CLASS.
    Hadjiiski L, Chan HP, Cohan RH, Caoili EM, Law Y, Cha K, Zhou C, Wei J.
    Med Phys; 2013 Nov; 40(11):111906. PubMed ID: 24320439
    [Abstract] [Full Text] [Related]

  • 5. CT urography: segmentation of urinary bladder using CLASS with local contour refinement.
    Cha K, Hadjiiski L, Chan HP, Caoili EM, Cohan RH, Zhou C.
    Phys Med Biol; 2014 Jun 07; 59(11):2767-85. PubMed ID: 24801066
    [Abstract] [Full Text] [Related]

  • 6. Automated pectoral muscle identification on MLO-view mammograms: Comparison of deep neural network to conventional computer vision.
    Ma X, Wei J, Zhou C, Helvie MA, Chan HP, Hadjiiski LM, Lu Y.
    Med Phys; 2019 May 07; 46(5):2103-2114. PubMed ID: 30771257
    [Abstract] [Full Text] [Related]

  • 7. Auto-initialized cascaded level set (AI-CALS) segmentation of bladder lesions on multidetector row CT urography.
    Hadjiiski L, Chan HP, Caoili EM, Cohan RH, Wei J, Zhou C.
    Acad Radiol; 2013 Feb 07; 20(2):148-55. PubMed ID: 23085411
    [Abstract] [Full Text] [Related]

  • 8. Hybrid U-Net-based deep learning model for volume segmentation of lung nodules in CT images.
    Wang Y, Zhou C, Chan HP, Hadjiiski LM, Chughtai A, Kazerooni EA.
    Med Phys; 2022 Nov 07; 49(11):7287-7302. PubMed ID: 35717560
    [Abstract] [Full Text] [Related]

  • 9. Detection of urinary bladder mass in CT urography with SPAN.
    Cha K, Hadjiiski L, Chan HP, Cohan RH, Caoili EM, Zhou C.
    Med Phys; 2015 Jul 07; 42(7):4271-84. PubMed ID: 26133625
    [Abstract] [Full Text] [Related]

  • 10. Automated segmentation of kidney and renal mass and automated detection of renal mass in CT urography using 3D U-Net-based deep convolutional neural network.
    Lin Z, Cui Y, Liu J, Sun Z, Ma S, Zhang X, Wang X.
    Eur Radiol; 2021 Jul 07; 31(7):5021-5031. PubMed ID: 33439313
    [Abstract] [Full Text] [Related]

  • 11. Segmentation of urinary bladder in CT urography.
    Hadjiiski L, Chan HP, Caoili EM, Cohan RH.
    Annu Int Conf IEEE Eng Med Biol Soc; 2012 Jul 07; 2012():3978-81. PubMed ID: 23366799
    [Abstract] [Full Text] [Related]

  • 12. Automated fibroglandular tissue segmentation in breast MRI using generative adversarial networks.
    Ma X, Wang J, Zheng X, Liu Z, Long W, Zhang Y, Wei J, Lu Y.
    Phys Med Biol; 2020 May 19; 65(10):105006. PubMed ID: 32155611
    [Abstract] [Full Text] [Related]

  • 13. 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 19; 46(2):576-589. PubMed ID: 30480818
    [Abstract] [Full Text] [Related]

  • 14. Two-stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra-reader and inter-reader reliability at full and reduced radiation dose on an external dataset.
    Panda A, Korfiatis P, Suman G, Garg SK, Polley EC, Singh DP, Chari ST, Goenka AH.
    Med Phys; 2021 May 19; 48(5):2468-2481. PubMed ID: 33595105
    [Abstract] [Full Text] [Related]

  • 15. Automatic bladder segmentation from CT images using deep CNN and 3D fully connected CRF-RNN.
    Xu X, Zhou F, Liu B.
    Int J Comput Assist Radiol Surg; 2018 Jul 19; 13(7):967-975. PubMed ID: 29556905
    [Abstract] [Full Text] [Related]

  • 16. Comparative study of algorithms for synthetic CT generation from MRI: Consequences for MRI-guided radiation planning in the pelvic region.
    Arabi H, Dowling JA, Burgos N, Han X, Greer PB, Koutsouvelis N, Zaidi H.
    Med Phys; 2018 Nov 19; 45(11):5218-5233. PubMed ID: 30216462
    [Abstract] [Full Text] [Related]

  • 17. 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 27; 14(1):213. PubMed ID: 31775825
    [Abstract] [Full Text] [Related]

  • 18. 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 27; 48(1):227-237. PubMed ID: 33151620
    [Abstract] [Full Text] [Related]

  • 19. Deep-learning-based method for the segmentation of ureter and renal pelvis on non-enhanced CT scans.
    Jin X, Zhong H, Zhang Y, Pang GD.
    Sci Rep; 2024 Aug 30; 14(1):20227. PubMed ID: 39215092
    [Abstract] [Full Text] [Related]

  • 20. Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography.
    Samala RK, Chan HP, Hadjiiski L, Helvie MA, Wei J, Cha K.
    Med Phys; 2016 Dec 30; 43(12):6654. PubMed ID: 27908154
    [Abstract] [Full Text] [Related]

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