BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

270 related articles for article (PubMed ID: 28161593)

  • 1. A generalized active shape model for segmentation of liver in low-contrast CT volumes.
    Esfandiarkhani M; Foruzan AH
    Comput Biol Med; 2017 Mar; 82():59-70. PubMed ID: 28161593
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model.
    He B; Huang C; Sharp G; Zhou S; Hu Q; Fang C; Fan Y; Jia F
    Med Phys; 2016 May; 43(5):2421. PubMed ID: 27147353
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Atlas-based automated segmentation of spleen and liver using adaptive enhancement estimation.
    Linguraru MG; Sandberg JK; Li Z; Pura JA; Summers RM
    Med Image Comput Comput Assist Interv; 2009; 12(Pt 2):1001-8. PubMed ID: 20426209
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation.
    Linguraru MG; Sandberg JK; Li Z; Shah F; Summers RM
    Med Phys; 2010 Feb; 37(2):771-83. PubMed ID: 20229887
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 3D liver segmentation using multiple region appearances and graph cuts.
    Peng J; Hu P; Lu F; Peng Z; Kong D; Zhang H
    Med Phys; 2015 Dec; 42(12):6840-52. PubMed ID: 26632041
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Liver segmentation using automatically defined patient specific B-spline surface models.
    Song Y; Bulpitt AJ; Brodlie KW
    Med Image Comput Comput Assist Interv; 2009; 12(Pt 2):43-50. PubMed ID: 20426094
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Joint optimization of segmentation and shape prior from level-set-based statistical shape model, and its application to the automated segmentation of abdominal organs.
    Saito A; Nawano S; Shimizu A
    Med Image Anal; 2016 Feb; 28():46-65. PubMed ID: 26716720
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Automated liver segmentation from a postmortem CT scan based on a statistical shape model.
    Saito A; Yamamoto S; Nawano S; Shimizu A
    Int J Comput Assist Radiol Surg; 2017 Feb; 12(2):205-221. PubMed ID: 27659283
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Segmentation of liver and spleen based on computational anatomy models.
    Dong C; Chen YW; Foruzan AH; Lin L; Han XH; Tateyama T; Wu X; Xu G; Jiang H
    Comput Biol Med; 2015 Dec; 67():146-60. PubMed ID: 26551453
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Automated segmentation of the liver from 3D CT images using probabilistic atlas and multilevel statistical shape model.
    Okada T; Shimada R; Hori M; Nakamoto M; Chen YW; Nakamura H; Sato Y
    Acad Radiol; 2008 Nov; 15(11):1390-403. PubMed ID: 18995190
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Blood vessel-based liver segmentation using the portal phase of an abdominal CT dataset.
    Maklad AS; Matsuhiro M; Suzuki H; Kawata Y; Niki N; Satake M; Moriyama N; Utsunomiya T; Shimada M
    Med Phys; 2013 Nov; 40(11):113501. PubMed ID: 24320472
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Automated segmentation of the liver from 3D CT images using probabilistic atlas and multi-level statistical shape model.
    Okada T; Shimada R; Sato Y; Hori M; Yokota K; Nakamoto M; Chen YW; Nakamura H; Tamura S
    Med Image Comput Comput Assist Interv; 2007; 10(Pt 1):86-93. PubMed ID: 18051047
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Locally-constrained boundary regression for segmentation of prostate and rectum in the planning CT images.
    Shao Y; Gao Y; Wang Q; Yang X; Shen D
    Med Image Anal; 2015 Dec; 26(1):345-56. PubMed ID: 26439938
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A conditional statistical shape model with integrated error estimation of the conditions; application to liver segmentation in non-contrast CT images.
    Tomoshige S; Oost E; Shimizu A; Watanabe H; Nawano S
    Med Image Anal; 2014 Jan; 18(1):130-43. PubMed ID: 24184436
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Three-dimensional lung tumor segmentation from x-ray computed tomography using sparse field active models.
    Awad J; Owrangi A; Villemaire L; O'Riordan E; Parraga G; Fenster A
    Med Phys; 2012 Feb; 39(2):851-65. PubMed ID: 22320795
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Minimal shape and intensity cost path segmentation.
    Seghers D; Loeckx D; Maes F; Vandermeulen D; Suetens P
    IEEE Trans Med Imaging; 2007 Aug; 26(8):1115-29. PubMed ID: 17695131
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Tracking fuzzy borders using geodesic curves with application to liver segmentation on planning CT.
    Yuan Y; Chao M; Sheu RD; Rosenzweig K; Lo YC
    Med Phys; 2015 Jul; 42(7):4015-26. PubMed ID: 26133602
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A generic probabilistic active shape model for organ segmentation.
    Wimmer A; Soza G; Hornegger J
    Med Image Comput Comput Assist Interv; 2009; 12(Pt 2):26-33. PubMed ID: 20426092
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Automatic X-ray landmark detection and shape segmentation via data-driven joint estimation of image displacements.
    Chen C; Xie W; Franke J; Grutzner PA; Nolte LP; Zheng G
    Med Image Anal; 2014 Apr; 18(3):487-99. PubMed ID: 24561486
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A shape-guided deformable model with evolutionary algorithm initialization for 3D soft tissue segmentation.
    Heimann T; Münzing S; Meinzer HP; Wolf I
    Inf Process Med Imaging; 2007; 20():1-12. PubMed ID: 17633684
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 14.