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Related Concept Videos

Skeletal Muscle Anatomy00:55

Skeletal Muscle Anatomy

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.
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
Gross Anatomy of Skeletal Muscles01:12

Gross Anatomy of Skeletal Muscles

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,...
Microscopic Anatomy of Skeletal Muscles01:13

Microscopic Anatomy of Skeletal Muscles

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...

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Related Experiment Video

Updated: Jul 2, 2026

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

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

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Refining Muscle Morphometry Through Machine Learning and Spatial Analysis.

Daisuke Ono1,2, Honami Kawai1, Hiroya Kuwahara1

  • 1Department of Neurology and Neurological Science, Institute of Science Tokyo, Bunkyo-Ku, Tokyo, Japan.

Neuropathology and Applied Neurobiology
|March 24, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning accurately quantifies muscle fiber morphology and spatial patterns in neuromuscular diseases. This automated approach improves diagnostic accuracy for myopathies and neuropathies compared to human analysis.

Keywords:
digital pathologymachine learningmuscle biopsymyopathymyositiswhole slide imaging

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Area of Science:

  • Neuromuscular pathology
  • Digital pathology
  • Machine learning in medicine

Background:

  • Muscle morphology is crucial for diagnosing neuromuscular diseases.
  • Current methods for muscle morphology analysis lack efficiency and objectivity.
  • Objective quantification of muscle fiber features is needed for accurate disease differentiation.

Purpose of the Study:

  • To develop and validate a machine learning-based system for automated muscle morphometry and spatial analysis.
  • To quantitatively refine morphological features of muscle fibers in neuromuscular diseases.
  • To improve the objective and efficient investigation of neuromuscular diseases.

Main Methods:

  • Retrospective analysis of 100 muscle biopsy specimens (haematoxylin and eosin-staining).
  • Development of machine learning software for muscle fiber segmentation, automated morphometry, and graph theory-based spatial analysis.
  • Training a LightGBM decision tree framework to predict disease aetiology using morphometric and spatial variables.

Main Results:

  • A YOLOv8 segmentation model achieved a mask average precision of 0.819.
  • Automated morphometry revealed distinct circularity patterns in myopathy and neuropathy groups.
  • The LightGBM model predicted diagnoses with 0.852 accuracy, outperforming human annotation (0.808).
  • Quantified grouped atrophy identified specific atrophy patterns and documented atypical presentations.

Conclusions:

  • Automated muscle morphometry and spatial analysis offer objective quantification of muscle morphology.
  • This approach facilitates efficient and accurate investigation of neuromuscular diseases.
  • Machine learning enhances diagnostic capabilities in muscle pathology.