Simulation of human lower limb skeletal muscle motion based on deep learning

2021 ◽  
Author(s):  
Xuesi Huang ◽  
Weilin Wang ◽  
Ravi Tomar
Keyword(s):  
Skeletal Muscle ◽  
Deep Learning ◽  
Lower Limb

scholarly journals Automated CT-derived skeletal muscle mass determination in lower hind limbs of mice using a 3D U-Net deep learning network

2020 ◽  
Vol 128 (1) ◽  
pp. 42-49 ◽  
Author(s):  
Brent van der Heyden ◽  
Wouter R. P. H. van de Worp ◽  
Ardy van Helvoort ◽  
Jan Theys ◽  
Annemie M. W. J. Schols ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Deep Learning ◽  
Muscle Mass ◽  
Lower Limb ◽  
Skeletal Muscle Mass ◽  
Learning Algorithm ◽  
Limb Muscle ◽  
Mass Measurements ◽  
Deep Learning Algorithm ◽  
Muscle Complex

The loss of skeletal muscle mass is recognized as a complication of several chronic diseases and is associated with increased mortality and a decreased quality of life. Relevant and reliable animal models in which muscle wasting can be monitored noninvasively over time are instrumental to investigate and develop new therapies. In this work, we developed a fully automatic deep learning algorithm for segmentation of micro cone beam computed tomography images of the lower limb muscle complex in mice and subsequent muscle mass calculation. A deep learning algorithm was trained on manually segmented data from 32 mice. Muscle wet mass measurements were obtained from 47 mice and served as a data set for model validation and reverse model validation. The automatic algorithm performance was ~150 times faster than manual segmentation. Reverse validation of the algorithm showed high quantitative metrics (i.e., a Dice similarity coefficient of 0.93, a Hausdorff distance of 0.4 mm, and a center of mass displacement of 0.1 mm), substantiating the robustness and accuracy of the model. A high correlation ( R2 = 0.92) was obtained between the computed tomography-derived muscle mass measurements and the muscle wet masses. Longitudinal follow-up revealed time-dependent changes in muscle mass that separated control from lung tumor-bearing mice, which was confirmed as cachexia. In conclusion, this deep learning model for automated assessment of the lower limb muscle complex provides highly accurate noninvasive longitudinal evaluation of skeletal muscle mass. Furthermore, it facilitates the workflow and increases the amount of data derived from mouse studies while reducing the animal numbers. NEW & NOTEWORTHY This deep learning application enables highly accurate noninvasive longitudinal evaluation of skeletal muscle mass changes in mice with minimal requirement for operator involvement in the data analysis. It provides a unique opportunity to increase and analyze the amount of data derived from animal studies automatically while reducing animal numbers and analytical workload.

3D Image Volumes From 2D Digitally Reconstructed X‐Rays: A Deep Learning Approach In Lower Limb CT‐Scans

2021 ◽  
Author(s):  
Diogo F. Almeida ◽  
Patricio Astudillo ◽  
Dirk Vandermeulen
Keyword(s):  
Deep Learning ◽  
Lower Limb ◽  
Ct Scans ◽  
Learning Approach ◽  
X Rays ◽  
3D Image

Intrinsic mitochondrial function in COPD is not impaired in lower limb skeletal muscle

2016 ◽  
Author(s):  
Bhavesh Popat ◽  
Tim Constantin ◽  
Despina Constantin ◽  
Lorna Latimer ◽  
Charlotte Bolton ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Mitochondrial Function ◽  
Lower Limb

Hypoxia-Inducible Factor 1 in Lower Limb Ischemia

Vascular ◽  
2006 ◽  
Vol 14 (6) ◽  
pp. 321-327 ◽  
Author(s):  
Teik K. Ho ◽  
David J. Abraham ◽  
Carol M. Black ◽  
Daryll M. Baker
Keyword(s):  
Skeletal Muscle ◽  
Lower Limb ◽  
Target Genes ◽  
Therapeutic Potential ◽  
Hypoxia Inducible Factor ◽  
Limb Ischemia ◽  
Limb Amputation ◽  
Western World ◽  
High Morbidity ◽  
Activity Increase

In the Western world, peripheral vascular disease (PVD) has a high prevalence and is associated with high morbidity and mortality. More patients are presenting with critical limb ischemia (CLI), the end stage of PVD, because of an increased life expectancy owing to improved medical care. In a large percentage of these patients, lower limb amputation is still required, despite current advances in surgery and interventional radiology. Studies of ischemic skeletal muscles disclosed evidence of endogenous angiogenesis and adaptive skeletal muscle metabolic changes in response to hypoxia. Many of the genes responsible for these responses are regulated by hypoxia-inducible factor (HIF)-1. HIF-1, consisting of HIF-1α and HIF-1β subunits, is a major transcription factor that functions as a master regulator of oxygen homeostasis that plays essential roles in cellular and systemic pathophysiology. HIF-1α expression and HIF-1 transcriptional activity increase exponentially as cellular oxygen concentration is decreased. More than 60 target genes that are transactivated by HIF-1 have been identified. Many of the target genes, such as vascular endothelial growth factor, have been studied extensively, especially in tumors. However, only recently that interest in HIF-1 is growing in relation to ischemic diseases. Most of the studies concentrated mainly on the angiogenic property of HIF-1. In contrast, there is a lack of information on the role of HIF-1 in skeletal muscle metabolic adaptive changes as the end-organ in PVD. This review aims to summarize our current understanding of HIF-1 roles and the therapeutic potential in PVD.

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