7/30/2023 0 Comments Thigh muscle compartments![]() This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: The source code for the proposed framework is available on a public GitHub repository ( ) and the data are available upon request from the University of Louisville Human Subjects Protection Program Office ( for researchers who meet the criteria to access the confidential data.įunding: This work was supported by the Leona M. Received: DecemAccepted: ApPublished: May 9, 2019Ĭopyright: © 2019 Mesbah et al. PLoS ONE 14(5):Įditor: Dzung Pham, Center for Neuroscience and Regenerative Medicine, UNITED STATES (2019) Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals. The automatic segmentation method proposed in this study can provide fast and accurate quantification of adipose and muscle tissues, which have important health and functional implications in the SCI population.Ĭitation: Mesbah S, Shalaby AM, Stills S, Soliman AM, Willhite A, Harkema SJ, et al. Also, the framework proposed in this study showed similar Dice accuracy and better Hausdorff distance measure to that obtained using DeepMedic Convolutional Neural Network structure, a well-known deep learning network for 3-D medical image segmentation. The proposed framework for muscle compartment segmentation showed an overall higher accuracy compared to ANTs and STAPLE, two previously validated atlas-based segmentation methods. The accuracy of the automatic segmentation method was tested both on SCI (N = 16) and on non-disabled (N = 14) individuals, showing an overall 0.93☐.06 accuracy for adipose tissue and muscle compartments segmentation based on Dice Similarity Coefficient. ![]() Also, three thigh muscle groups were segmented utilizing the proposed 3-D Joint Markov Gibbs Random Field model that integrates first order appearance model, spatial information, and shape model to localize the muscle groups. In this framework, subcutaneous adipose tissue, inter-muscular adipose tissue and total muscle tissue are segmented using linear combination of discrete Gaussians algorithm. In this study, we developed a novel automatic 3-D approach for volumetric segmentation and quantitative assessment of thigh Magnetic Resonance Imaging (MRI) volumes in individuals with chronic SCI as well as non-disabled individuals. Severe spinal cord injury (SCI) leads to skeletal muscle atrophy and adipose tissue infiltration in the skeletal muscle, which can result in compromised muscle mechanical output and lead to health-related complications.
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