Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Relaxation of Skeletal Muscles01:29

Relaxation of Skeletal Muscles

5.3K
The period of muscle contraction primarily influences the duration of stimulation at the neuromuscular junction (NMJ), the presence of free calcium ions in the sarcoplasm, and the availability of energy or ATP to support contractions.
When an action potential reaches the axon terminal, it depolarizes the membrane and opens voltage-gated sodium channels. Sodium ions enter the cell, further depolarizing the presynaptic membrane. This depolarization causes voltage-gated calcium channels to open....
5.3K
Gross Anatomy of Skeletal Muscles01:12

Gross Anatomy of Skeletal Muscles

18.6K
The connective tissues play a significant role in arranging the muscle fibers into a hierarchical structure that forms a complete muscle. Consider a muscle like the bicep brachii, commonly called the bicep. This muscle comprises thousands of muscle fibers enclosed by a protective layer of connective tissue called the endomysium. The endomysium is primarily composed of reticular fibers, a type of thin collagen fiber. It allows the exchange of nutrients and waste products at the fiber level,...
18.6K
Microscopic Anatomy of Skeletal Muscles01:13

Microscopic Anatomy of Skeletal Muscles

21.7K
Skeletal muscle cells, also called muscle fibers, are distinctly elongated, multi-nucleated, slender biological units. They are packed with specialized structures designed to facilitate their primary function, which is contraction.
The muscle sarcolemma is a plasma membrane enclosing each muscle cell that conducts electrical signals called action potentials. The sarcolemma extends into the cell to form T-tubules, ensuring the neural impulses are uniformly distributed across the entire muscle...
21.7K
Skeletal Muscle Anatomy00:55

Skeletal Muscle Anatomy

92.4K
Skeletal muscle is the most abundant type of muscle in the body. Tendons are the connective tissue that attaches skeletal muscle to bones. Skeletal muscles pull on tendons, which in turn pull on bones to carry out voluntary movements.
92.4K
Classification of Bones01:18

Classification of Bones

9.3K
The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
9.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

[Site-specific Segmentation of Skeletal Muscles in Body CT Images via Comprehensive Muscular Consideration].

Nihon Hoshasen Gijutsu Gakkai zasshi·2026
Same author

Single-Molecule 3D Nanoscopy for Application to Cryofixed Samples.

The journal of physical chemistry. B·2025
Same author

Joint segmentation of sternocleidomastoid and skeletal muscles in computed tomography images using a multiclass learning approach.

Radiological physics and technology·2024
Same author

Erratum: "Superfluid helium nanoscope insert with millimeter working range" [Rev. Sci. Instrum. 93, 103703 (2022)].

The Review of scientific instruments·2022
Same author

Superfluid helium nanoscope insert with millimeter working range.

The Review of scientific instruments·2022
Same author

Visualizing the Flow Patterns in an Expanding and Contracting Pulmonary Alveolated Duct Based on Microcomputed Tomography Images.

Journal of biomechanical engineering·2021
Same journal

Peptidomics in the Spotlight: Advanced Sample Treatment Techniques and Analytical Insights.

Advances in experimental medicine and biology·2026
Same journal

Methods for the Investigation of Protein-Ligands Interactions.

Advances in experimental medicine and biology·2026
Same journal

Sample Preparation Strategies for Microbial Cell Surface Proteomics: Integrating Shaving and Shotgun Approaches.

Advances in experimental medicine and biology·2026
Same journal

Proteomic Sample Preparation for the Petroleum Industry: A Biocorrosion Case Study.

Advances in experimental medicine and biology·2026
Same journal

Proteomic and Functional Comparison of Extracellular Vesicles from Wild-Type and Lyn-Deficient Stromal Cells.

Advances in experimental medicine and biology·2026
Same journal

Proteomic Analysis of Histone Sequence Variants and Post-translationally Modified Forms.

Advances in experimental medicine and biology·2026
See all related articles

Related Experiment Video

Updated: Dec 29, 2025

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

10.1K

Deep Learning Technique for Musculoskeletal Analysis.

Naoki Kamiya1

  • 1School of Information Science and Technology, Aichi Prefectural University, Nagakute, Japan. n-kamiya@ist.aichi-pu.ac.jp.

Advances in Experimental Medicine and Biology
|February 8, 2020
PubMed
Summary
This summary is machine-generated.

Deep learning enhances musculoskeletal analysis by automatically recognizing skeletal muscles from whole-body CT images. This approach aids in classifying atrophic muscular diseases using image features alone.

Keywords:
2D U-Net3D U-NetDeep muscleFCN-8sMusculoskeletal analysisMusculoskeletal segmentationRandom forestSkeletal muscleSurface muscle

More Related Videos

3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue
08:52

3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue

Published on: November 27, 2017

23.8K
A Novel Application of Musculoskeletal Ultrasound Imaging
10:53

A Novel Application of Musculoskeletal Ultrasound Imaging

Published on: September 17, 2013

24.5K

Related Experiment Videos

Last Updated: Dec 29, 2025

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

10.1K
3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue
08:52

3D Ultrasound Imaging: Fast and Cost-effective Morphometry of Musculoskeletal Tissue

Published on: November 27, 2017

23.8K
A Novel Application of Musculoskeletal Ultrasound Imaging
10:53

A Novel Application of Musculoskeletal Ultrasound Imaging

Published on: September 17, 2013

24.5K

Area of Science:

  • Biomedical Engineering
  • Radiology
  • Computer Science

Background:

  • Musculoskeletal analysis traditionally relies on computational anatomy, which is labor-intensive.
  • Conventional methods for skeletal muscle recognition using handcrafted features have limitations.
  • Deep learning offers automated muscle information extraction from medical images.

Purpose of the Study:

  • To review advancements in musculoskeletal analysis using deep learning.
  • To present a technique for whole-body computed tomography (CT) image analysis for musculoskeletal systems.
  • To demonstrate automatic skeletal muscle recognition and atrophic muscular disease classification via deep learning.

Main Methods:

  • Discussing the necessity of musculoskeletal analysis and image processing requirements.
  • Evaluating limitations of handcrafted features in skeletal muscle recognition.
  • Applying deep learning techniques to whole-body CT scans for automated analysis.
  • Utilizing image features for automatic classification of atrophic muscular diseases.

Main Results:

  • Deep learning enables automatic extraction of muscle characteristics like shape, volume, and area.
  • Whole-body CT image analysis facilitates comprehensive musculoskeletal assessment.
  • The study demonstrates the potential for automated skeletal muscle recognition and disease classification.

Conclusions:

  • Deep learning significantly advances musculoskeletal analysis, offering automated feature extraction and disease classification.
  • Combining deep learning with handcrafted features may offer future synergistic benefits for musculoskeletal modeling.
  • Automated analysis of whole-body CT images holds promise for improved diagnostics and personalized medicine.