BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

393 related articles for article (PubMed ID: 28375584)

  • 1. Fully automated segmentation of whole breast using dynamic programming in dynamic contrast enhanced MR images.
    Jiang L; Hu X; Xiao Q; Gu Y; Li Q
    Med Phys; 2017 Jun; 44(6):2400-2414. PubMed ID: 28375584
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Segmentation of whole breast and fibroglandular tissue using nnU-Net in dynamic contrast enhanced MR images.
    Huo L; Hu X; Xiao Q; Gu Y; Chu X; Jiang L
    Magn Reson Imaging; 2021 Oct; 82():31-41. PubMed ID: 34147598
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method.
    Wu S; Weinstein SP; Conant EF; Kontos D
    Med Phys; 2013 Dec; 40(12):122302. PubMed ID: 24320533
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Automated chest wall line detection for whole-breast segmentation in sagittal breast MR images.
    Wu S; Weinstein SP; Conant EF; Schnall MD; Kontos D
    Med Phys; 2013 Apr; 40(4):042301. PubMed ID: 23556914
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Fully automatic quantification of fibroglandular tissue and background parenchymal enhancement with accurate implementation for axial and sagittal breast MRI protocols.
    Wei D; Jahani N; Cohen E; Weinstein S; Hsieh MK; Pantalone L; Kontos D
    Med Phys; 2021 Jan; 48(1):238-252. PubMed ID: 33150617
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Fully Automatic Assessment of Background Parenchymal Enhancement on Breast MRI Using Machine-Learning Models.
    Nam Y; Park GE; Kang J; Kim SH
    J Magn Reson Imaging; 2021 Mar; 53(3):818-826. PubMed ID: 33219624
    [TBL] [Abstract][Full Text] [Related]  

  • 7. SU-E-I-70: Semi-Automatic, User-Driven Breast, Chest Wall and FGT Segmentations Based on Hough Transform, Morphology Tools and Histogram Technology.
    Wang Y; Deasy J
    Med Phys; 2012 Jun; 39(6Part5):3641. PubMed ID: 28517626
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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; 65(10):105006. PubMed ID: 32155611
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Using deep learning to segment breast and fibroglandular tissue in MRI volumes.
    Dalmış MU; Litjens G; Holland K; Setio A; Mann R; Karssemeijer N; Gubern-Mérida A
    Med Phys; 2017 Feb; 44(2):533-546. PubMed ID: 28035663
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Automated breast-region segmentation in the axial breast MR images.
    Milenković J; Chambers O; Marolt Mušič M; Tasič JF
    Comput Biol Med; 2015 Jul; 62():55-64. PubMed ID: 25912987
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Treatment response assessment of breast masses on dynamic contrast-enhanced magnetic resonance scans using fuzzy c-means clustering and level set segmentation.
    Shi J; Sahiner B; Chan HP; Paramagul C; Hadjiiski LM; Helvie M; Chenevert T
    Med Phys; 2009 Nov; 36(11):5052-63. PubMed ID: 19994516
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Generalizable attention U-Net for segmentation of fibroglandular tissue and background parenchymal enhancement in breast DCE-MRI.
    Nowakowska S; Borkowski K; Ruppert CM; Landsmann A; Marcon M; Berger N; Boss A; Ciritsis A; Rossi C
    Insights Imaging; 2023 Nov; 14(1):185. PubMed ID: 37932462
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Fully Automated Convolutional Neural Network Method for Quantification of Breast MRI Fibroglandular Tissue and Background Parenchymal Enhancement.
    Ha R; Chang P; Mema E; Mutasa S; Karcich J; Wynn RT; Liu MZ; Jambawalikar S
    J Digit Imaging; 2019 Feb; 32(1):141-147. PubMed ID: 30076489
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Knowledge-based and deep learning-based automated chest wall segmentation in magnetic resonance images of extremely dense breasts.
    Verburg E; Wolterink JM; de Waard SN; Išgum I; van Gils CH; Veldhuis WB; Gilhuijs KGA
    Med Phys; 2019 Oct; 46(10):4405-4416. PubMed ID: 31274194
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Automated deep learning method for whole-breast segmentation in diffusion-weighted breast MRI.
    Zhang L; Mohamed AA; Chai R; Guo Y; Zheng B; Wu S
    J Magn Reson Imaging; 2020 Feb; 51(2):635-643. PubMed ID: 31301201
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Automated breast segmentation of fat and water MR images using dynamic programming.
    Rosado-Toro JA; Barr T; Galons JP; Marron MT; Stopeck A; Thomson C; Thompson P; Carroll D; Wolf E; Altbach MI; Rodríguez JJ
    Acad Radiol; 2015 Feb; 22(2):139-48. PubMed ID: 25572926
    [TBL] [Abstract][Full Text] [Related]  

  • 17. An unsupervised automatic segmentation algorithm for breast tissue classification of dedicated breast computed tomography images.
    Caballo M; Boone JM; Mann R; Sechopoulos I
    Med Phys; 2018 Jun; 45(6):2542-2559. PubMed ID: 29676025
    [TBL] [Abstract][Full Text] [Related]  

  • 18. An automated skin segmentation of Breasts in Dynamic Contrast-Enhanced Magnetic Resonance Imaging.
    Lee CY; Chang TF; Chang NY; Chang YC
    Sci Rep; 2018 Apr; 8(1):6159. PubMed ID: 29670156
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Quantitative assessment of background parenchymal enhancement in breast MRI predicts response to risk-reducing salpingo-oophorectomy: preliminary evaluation in a cohort of BRCA1/2 mutation carriers.
    Wu S; Weinstein SP; DeLeo MJ; Conant EF; Chen J; Domchek SM; Kontos D
    Breast Cancer Res; 2015 May; 17():67. PubMed ID: 25986460
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Malignant lesion segmentation in contrast-enhanced breast MR images based on the marker-controlled watershed.
    Cui Y; Tan Y; Zhao B; Liberman L; Parbhu R; Kaplan J; Theodoulou M; Hudis C; Schwartz LH
    Med Phys; 2009 Oct; 36(10):4359-69. PubMed ID: 19928066
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 20.