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 *

109 related articles for article (PubMed ID: 34823014)

  • 1. Local MRI-based measures of thigh adipose tissue derived from fully automated deep convolutional neural network-based segmentation show a comparable responsiveness to bidirectional change in body weight as from quality controlled manual segmentation.
    Kemnitz J; Steidle-Kloc E; Wirth W; Fuerst D; Wisser A; Eder SK; Eckstein F
    Ann Anat; 2022 Feb; 240():151866. PubMed ID: 34823014
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Responsiveness of Subcutaneous Fat, Intermuscular Fat, and Muscle Anatomical Cross-Sectional Area of the Thigh to Longitudinal Body Weight Loss and Gain: Data from the Osteoarthritis Initiative (OAI).
    Steidle-Kloc E; Dannhauer T; Wirth W; Eckstein F
    Cells Tissues Organs; 2022; 211(5):555-564. PubMed ID: 34619678
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Clinical evaluation of fully automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain.
    Kemnitz J; Baumgartner CF; Eckstein F; Chaudhari A; Ruhdorfer A; Wirth W; Eder SK; Konukoglu E
    MAGMA; 2020 Aug; 33(4):483-493. PubMed ID: 31872357
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Quantitative relationship of thigh adipose tissue with pain, radiographic status, and progression of knee osteoarthritis: longitudinal findings from the osteoarthritis initiative.
    Dannhauer T; Ruhdorfer A; Wirth W; Eckstein F
    Invest Radiol; 2015 Apr; 50(4):268-74. PubMed ID: 25419827
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Relationships between fatty infiltration in the thigh and calf in women with knee osteoarthritis.
    Davison MJ; Maly MR; Adachi JD; Noseworthy MD; Beattie KA
    Aging Clin Exp Res; 2017 Apr; 29(2):291-299. PubMed ID: 26964549
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Longitudinal (4 year) change of thigh muscle and adipose tissue distribution in chronically painful vs painless knees--data from the Osteoarthritis Initiative.
    Ruhdorfer A; Wirth W; Dannhauer T; Eckstein F
    Osteoarthritis Cartilage; 2015 Aug; 23(8):1348-56. PubMed ID: 25887367
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Responsiveness of Infrapatellar Fat Pad Volume Change to Body Weight Loss or Gain: Data from the Osteoarthritis Initiative.
    Steidle-Kloc E; Dannhauer T; Wirth W; Eckstein F
    Cells Tissues Organs; 2018; 205(1):53-62. PubMed ID: 29393196
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Thigh muscle composition changes in knee osteoarthritis patients during weight loss: Sex-specific analysis using data from osteoarthritis initiative.
    Moradi K; Mohajer B; Mohammadi S; Guermazi A; Ibad HA; Roemer FW; Cao X; Link TM; Demehri S
    Osteoarthritis Cartilage; 2024 Sep; 32(9):1154-1162. PubMed ID: 38851527
    [TBL] [Abstract][Full Text] [Related]  

  • 10. The optimisation of deep neural networks for segmenting multiple knee joint tissues from MRIs.
    Kessler DA; MacKay JW; Crowe VA; Henson FMD; Graves MJ; Gilbert FJ; Kaggie JD
    Comput Med Imaging Graph; 2020 Dec; 86():101793. PubMed ID: 33075675
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Quantifying fat and lean muscle in the lower legs of women with knee osteoarthritis using two different MRI systems.
    Beattie K; Davison MJ; Noseworthy M; Adachi JD; Maly MR
    Rheumatol Int; 2016 Jun; 36(6):855-62. PubMed ID: 26979605
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Quantitative MRI outcome measures in CMT1A using automated lower limb muscle segmentation.
    O'Donnell LF; Pipis M; Thornton JS; Kanber B; Wastling S; McDowell A; Zafeiropoulos N; Laura M; Skorupinska M; Record CJ; Doherty CM; Herrmann DN; Zetterberg H; Heslegrave AJ; Laban R; Rossor AM; Morrow JM; Reilly MM
    J Neurol Neurosurg Psychiatry; 2024 May; 95(6):500-503. PubMed ID: 37979968
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Precision, Reliability, and Responsiveness of a Novel Automated Quantification Tool for Cartilage Thickness: Data from the Osteoarthritis Initiative.
    Bowes MA; Guillard GA; Vincent GR; Brett AD; Wolstenholme CBH; Conaghan PG
    J Rheumatol; 2020 Feb; 47(2):282-289. PubMed ID: 30988122
    [TBL] [Abstract][Full Text] [Related]  

  • 15. The role of thigh muscle and adipose tissue in knee osteoarthritis progression in women: data from the Osteoarthritis Initiative.
    Kemnitz J; Wirth W; Eckstein F; Culvenor AG
    Osteoarthritis Cartilage; 2018 Sep; 26(9):1190-1195. PubMed ID: 29890261
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Fully convolutional networks for automated segmentation of abdominal adipose tissue depots in multicenter water-fat MRI.
    Langner T; Hedström A; Mörwald K; Weghuber D; Forslund A; Bergsten P; Ahlström H; Kullberg J
    Magn Reson Med; 2019 Apr; 81(4):2736-2745. PubMed ID: 30311704
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Convolutional neural network-based segmentation can help in assessing the substantia nigra in neuromelanin MRI.
    Le Berre A; Kamagata K; Otsuka Y; Andica C; Hatano T; Saccenti L; Ogawa T; Takeshige-Amano H; Wada A; Suzuki M; Hagiwara A; Irie R; Hori M; Oyama G; Shimo Y; Umemura A; Hattori N; Aoki S
    Neuroradiology; 2019 Dec; 61(12):1387-1395. PubMed ID: 31401723
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A Valid and Precise Semiautomated Method for Quantifying Intermuscular Fat Intramuscular Fat in Lower Leg Magnetic Resonance Images.
    Wong AKO; Szabo E; Erlandson M; Sussman MS; Duggina S; Song A; Reitsma S; Gillick H; Adachi JD; Cheung AM
    J Clin Densitom; 2020; 23(4):611-622. PubMed ID: 30352783
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Statin use and longitudinal changes in quantitative MRI-based biomarkers of thigh muscle quality: data from Osteoarthritis Initiative.
    Mohajer B; Moradi K; Guermazi A; Dolatshahi M; Roemer FW; Ibad HA; Parastooei G; Conaghan PG; Zikria BA; Wan M; Cao X; Lima JAC; Demehri S
    Skeletal Radiol; 2024 Apr; 53(4):683-695. PubMed ID: 37840051
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks.
    Krüger J; Opfer R; Gessert N; Ostwaldt AC; Manogaran P; Kitzler HH; Schlaefer A; Schippling S
    Neuroimage Clin; 2020; 28():102445. PubMed ID: 33038667
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.