These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

167 related articles for article (PubMed ID: 31071158)

  • 1. Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals.
    Mesbah S; Shalaby AM; Stills S; Soliman AM; Willhite A; Harkema SJ; Rejc E; El-Baz AS
    PLoS One; 2019; 14(5):e0216487. PubMed ID: 31071158
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Convolutional Neural Network-Based Automated Segmentation of the Spinal Cord and Contusion Injury: Deep Learning Biomarker Correlates of Motor Impairment in Acute Spinal Cord Injury.
    McCoy DB; Dupont SM; Gros C; Cohen-Adad J; Huie RJ; Ferguson A; Duong-Fernandez X; Thomas LH; Singh V; Narvid J; Pascual L; Kyritsis N; Beattie MS; Bresnahan JC; Dhall S; Whetstone W; Talbott JF;
    AJNR Am J Neuroradiol; 2019 Apr; 40(4):737-744. PubMed ID: 30923086
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Superpixel-based deep convolutional neural networks and active contour model for automatic prostate segmentation on 3D MRI scans.
    da Silva GLF; Diniz PS; Ferreira JL; França JVF; Silva AC; de Paiva AC; de Cavalcanti EAA
    Med Biol Eng Comput; 2020 Sep; 58(9):1947-1964. PubMed ID: 32566988
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Fully-integrated framework for the segmentation and registration of the spinal cord white and gray matter.
    Dupont SM; De Leener B; Taso M; Le Troter A; Nadeau S; Stikov N; Callot V; Cohen-Adad J
    Neuroimage; 2017 Apr; 150():358-372. PubMed ID: 27663988
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A fast stochastic framework for automatic MR brain images segmentation.
    Ismail M; Soliman A; Ghazal M; Switala AE; Gimel'farb G; Barnes GN; Khalil A; El-Baz A
    PLoS One; 2017; 12(11):e0187391. PubMed ID: 29136034
    [TBL] [Abstract][Full Text] [Related]  

  • 7. In-depth learning of automatic segmentation of shoulder joint magnetic resonance images based on convolutional neural networks.
    Mu X; Cui Y; Bian R; Long L; Zhang D; Wang H; Shen Y; Wu J; Zou G
    Comput Methods Programs Biomed; 2021 Nov; 211():106325. PubMed ID: 34536635
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Volume measurements of individual muscles in human quadriceps femoris using atlas-based segmentation approaches.
    Le Troter A; Fouré A; Guye M; Confort-Gouny S; Mattei JP; Gondin J; Salort-Campana E; Bendahan D
    MAGMA; 2016 Apr; 29(2):245-57. PubMed ID: 26983429
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Analysis of diffusion tensor measurements of the human cervical spinal cord based on semiautomatic segmentation of the white and gray matter.
    Dostál M; Keřkovský M; Korit Áková E; Němcová E; Stulík J; Staňková M; Bernard V
    J Magn Reson Imaging; 2018 Nov; 48(5):1217-1227. PubMed ID: 29707834
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Spinal Cord Segmentation by One Dimensional Normalized Template Matching: A Novel, Quantitative Technique to Analyze Advanced Magnetic Resonance Imaging Data.
    Cadotte A; Cadotte DW; Livne M; Cohen-Adad J; Fleet D; Mikulis D; Fehlings MG
    PLoS One; 2015; 10(10):e0139323. PubMed ID: 26445367
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Deep learning for automatic segmentation of thigh and leg muscles.
    Agosti A; Shaqiri E; Paoletti M; Solazzo F; Bergsland N; Colelli G; Savini G; Muzic SI; Santini F; Deligianni X; Diamanti L; Monforte M; Tasca G; Ricci E; Bastianello S; Pichiecchio A
    MAGMA; 2022 Jun; 35(3):467-483. PubMed ID: 34665370
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Deep learning-based automatic pipeline for quantitative assessment of thigh muscle morphology and fatty infiltration.
    Gaj S; Eck BL; Xie D; Lartey R; Lo C; Zaylor W; Yang M; Nakamura K; Winalski CS; Spindler KP; Li X
    Magn Reson Med; 2023 Jun; 89(6):2441-2455. PubMed ID: 36744695
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images.
    Yang YX; Chong MS; Tay L; Yew S; Yeo A; Tan CH
    MAGMA; 2016 Oct; 29(5):723-31. PubMed ID: 27026244
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Deep convolutional neural network for segmentation of thoracic organs-at-risk using cropped 3D images.
    Feng X; Qing K; Tustison NJ; Meyer CH; Chen Q
    Med Phys; 2019 May; 46(5):2169-2180. PubMed ID: 30830685
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Convolutional Neural Network-Based Automated Segmentation of Skeletal Muscle and Subcutaneous Adipose Tissue on Thigh MRI in Muscular Dystrophy Patients.
    Aringhieri G; Astrea G; Marfisi D; Fanni SC; Marinella G; Pasquariello R; Ricci G; Sansone F; Sperti M; Tonacci A; Torri F; Matà S; Siciliano G; Neri E; Santorelli FM; Conte R
    J Funct Morphol Kinesiol; 2024 Jul; 9(3):. PubMed ID: 39051284
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Deep network and multi-atlas segmentation fusion for delineation of thigh muscle groups in three-dimensional water-fat separated MRI.
    Annasamudram NV; Okorie AM; Spencer RG; Kalyani RR; Yang Q; Landman BA; Ferrucci L; Makrogiannis S
    J Med Imaging (Bellingham); 2024 Sep; 11(5):054003. PubMed ID: 39234425
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Validation of an active shape model-based semi-automated segmentation algorithm for the analysis of thigh muscle and adipose tissue cross-sectional areas.
    Kemnitz J; Eckstein F; Culvenor AG; Ruhdorfer A; Dannhauer T; Ring-Dimitriou S; Sänger AM; Wirth W
    MAGMA; 2017 Oct; 30(5):489-503. PubMed ID: 28455629
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Fully automatic 3D segmentation of the thoracolumbar spinal cord and the vertebral canal from T2-weighted MRI using K-means clustering algorithm.
    Sabaghian S; Dehghani H; Batouli SAH; Khatibi A; Oghabian MA
    Spinal Cord; 2020 Jul; 58(7):811-820. PubMed ID: 32132652
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Automatic muscle and fat segmentation in the thigh from T1-Weighted MRI.
    Orgiu S; Lafortuna CL; Rastelli F; Cadioli M; Falini A; Rizzo G
    J Magn Reson Imaging; 2016 Mar; 43(3):601-10. PubMed ID: 26268693
    [TBL] [Abstract][Full Text] [Related]  

  • 20. The Generalized Log-Ratio Transformation: Learning Shape and Adjacency Priors for Simultaneous Thigh Muscle Segmentation.
    Andrews S; Hamarneh G
    IEEE Trans Med Imaging; 2015 Sep; 34(9):1773-87. PubMed ID: 25700442
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.