AIMC Topic: Thigh
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Importance of neural network complexity for the automatic segmentation of individual thigh muscles in MRI images from patients with neuromuscular diseases.
Magma (New York, N.Y.)
Jan 11, 2025
OBJECTIVE: Segmentation of individual thigh muscles in MRI images is essential for monitoring neuromuscular diseases and quantifying relevant biomarkers such as fat fraction (FF). Deep learning approaches such as U-Net have demonstrated effectiveness...
Adult
Male
Middle Aged
Deep Learning
Female
Reproducibility of Results
Aged
Adipose Tissue
Thigh
Image Interpretation, Computer-Assisted
Neuromuscular Diseases
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Muscle, Skeletal
Algorithms
Databases, Factual
Humans
Neural Networks, Computer
Correlation between individual thigh muscle volume and grip strength in relation to sarcopenia with automated muscle segmentation.
PloS one
Dec 26, 2024
INTRODUCTION: Grip strength serves as a significant marker for diagnosing and assessing sarcopenia, particularly in elderly populations. The study aims to explore the relationship between individual thigh muscle volumes and grip strength, leveraging ...
Automatic segmentation of lower limb muscles from MR images of post-menopausal women based on deep learning and data augmentation.
PloS one
Apr 2, 2024
Individual muscle segmentation is the process of partitioning medical images into regions representing each muscle. It can be used to isolate spatially structured quantitative muscle characteristics, such as volume, geometry, and the level of fat inf...
The Impact of Fatty Infiltration on MRI Segmentation of Lower Limb Muscles in Neuromuscular Diseases: A Comparative Study of Deep Learning Approaches.
Journal of magnetic resonance imaging : JMRI
Apr 6, 2023
BACKGROUND: Deep learning methods have been shown to be useful for segmentation of lower limb muscle MRIs of healthy subjects but, have not been sufficiently evaluated on neuromuscular disease (NDM) patients.
Quantification of Comfort for the Development of Binding Parts in a Standing Rehabilitation Robot.
Sensors (Basel, Switzerland)
Feb 16, 2023
Human-machine interfaces (HMI) refer to the physical interaction between a user and rehabilitation robots. A persisting excessive load leads to soft tissue damage, such as pressure ulcers. Therefore, it is necessary to define a comfortable binding pa...
Deep learning-based automatic pipeline for quantitative assessment of thigh muscle morphology and fatty infiltration.
Magnetic resonance in medicine
Feb 6, 2023
PURPOSE: Fast and accurate thigh muscle segmentation from MRI is important for quantitative assessment of thigh muscle morphology and composition. A novel deep learning (DL) based thigh muscle and surrounding tissues segmentation model was developed ...
Deep learning-based fully automated body composition analysis of thigh CT: comparison with DXA measurement.
European radiology
Apr 18, 2022
OBJECTIVES: To compare volumetric CT with DL-based fully automated segmentation and dual-energy X-ray absorptiometry (DXA) in the measurement of thigh tissue composition.
Deep learning for automatic segmentation of thigh and leg muscles.
Magma (New York, N.Y.)
Oct 19, 2021
OBJECTIVE: In this study we address the automatic segmentation of selected muscles of the thigh and leg through a supervised deep learning approach.
A deep learning model for diagnosing dystrophinopathies on thigh muscle MRI images.
BMC neurology
Jan 11, 2021
BACKGROUND: Dystrophinopathies are the most common type of inherited muscular diseases. Muscle biopsy and genetic tests are effective to diagnose the disease but cost much more than primary hospitals can reach. The more available muscle MRI is promis...
Automated Muscle Segmentation from Clinical CT Using Bayesian U-Net for Personalized Musculoskeletal Modeling.
IEEE transactions on medical imaging
Sep 10, 2019
We propose a method for automatic segmentation of individual muscles from a clinical CT. The method uses Bayesian convolutional neural networks with the U-Net architecture, using Monte Carlo dropout that infers an uncertainty metric in addition to th...
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