Fully automated patellofemoral MRI segmentation using holistically nested networks: Implications for evaluating patellofemoral osteoarthritis, pain, injury, pathology, and adolescent development-Reference-Cited by-同舟云学术

Fully automated patellofemoral MRI segmentation using holistically nested networks: Implications for evaluating patellofemoral osteoarthritis, pain, injury, pathology, and adolescent development

Published:2019-08-11 Issue:1 Volume:83 Page:139-153
ISSN:0740-3194
Container-title:Magnetic Resonance in Medicine
language:en
Short-container-title:Magn Reson Med

Author:

Cheng Ruida¹^ORCID,Alexandridi Natalia A.²,Smith Richard M.²,Shen Aricia²³,Gandler William¹,McCreedy Evan¹,McAuliffe Matthew J.¹,Sheehan Frances T.²

Affiliation:

1. Biomedical Imaging Research Services Section (BIRSS) Office of Intramural Research Center of Information Technology NIH Bethesda Maryland

2. Functional and Applied Biomechanics Department of Rehabilitation Medicine NIH Bethesda Maryland

3. University of California Irvine School of Medicine Irvine California

Funder

National Institutes of Health

National Institute of Biomedical Imaging and Bioengineering

Foundation for the National Institutes of Health

Pfizer

Leona M. and Harry B. Helmsley Charitable Trust

Howard Hughes Medical Institute

Nvidia

National Institute of Child Health and Human Development

Publisher

Wiley

Subject

Radiology Nuclear Medicine and imaging

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.27920

Reference48 articles.

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2. The impact of musculoskeletal diseases on mortality–comparison with internal diseases: A 15-year longitudinal study

3. Radiological Assessment of Osteo-Arthrosis

4. Fully automated, level set-based segmentation for knee MRIs using an adaptive force function and template: data from the osteoarthritis initiative

5. LOGISMOS—Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces: Cartilage Segmentation in the Knee Joint

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