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Related Concept Videos

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The muscles that move the forearms can be divided into four groups: forearm flexors, forearm extensors, forearm pronators, and forearm supinators. The flexors and extensors act on the elbow joint, while the pronators and supinators act on the radioulnar joints.
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Smooth muscles are an important type of muscle tissue that plays a vital role in the involuntary movements of internal organs. For example, they help regulate the movement of food through the gut and the flow of blood through the circulatory system.
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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.
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The muscles that move the head are a dynamic and complex group of structures that work together to facilitate a wide range of head movements, including rotation, flexion, extension, and lateral bending.
The bilateral sternocleidomastoid, or SCM, and the suprahyoid and infrahyoid muscles are significant head flexors. The SCM muscles originate at the sternum and clavicle and attach to the mastoid process of the temporal bone. The SCM contracts bilaterally to bend the head forward, whereas...
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Automated Segmentation of Forearm Muscles: Clinical Associations With Hand Function, Muscle Volume and Intramuscular

Joel Fundaun1, Valeria Oliva1,2, Sandrine Bédard1,3

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Automated MRI segmentation accurately assesses forearm muscle health, linking muscle volume to grip strength. This computer vision approach aids in evaluating conditions affecting hand function.

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Area of Science:

  • Biomedical Engineering
  • Radiology
  • Computer Vision

Background:

  • Hand function is crucial for daily activities and is often impaired early in musculoskeletal and neurological diseases.
  • Muscle health markers from magnetic resonance imaging (MRI) correlate with functional capacity but traditional assessments are time-intensive.
  • Automated segmentation using computer vision offers a potential solution for efficient muscle health assessment.

Purpose of the Study:

  • To develop and validate a computer-vision model for automated forearm muscle segmentation using MRI.
  • To investigate the associations between MRI-derived muscle markers (volume, intramuscular fat) and demographic factors (age, sex, BMI).
  • To explore the relationship between muscle markers and functional measures like grip strength and dexterity.

Main Methods:

  • A 2D U-Net convolutional neural network was trained on manually segmented forearm muscles from Dixon fat-water MRI scans.
  • The model's accuracy and reliability were tested using Sørensen-Dice indices and intraclass correlation coefficients (ICCs).
  • Associations between muscle metrics, demographics, and hand function were analyzed using correlation and regression models.

Main Results:

  • The automated segmentation model demonstrated high accuracy (Sørensen-Dice indices 0.85-0.89) and reliability (ICCs 0.75-0.99).
  • Muscle volume correlated positively with BMI and was larger in males, independent of age.
  • Grip strength showed strong positive correlations with muscle volumes, while intramuscular fat was not associated with function.

Conclusions:

  • Automated forearm muscle segmentation via computer vision is accurate and reliable.
  • Muscle volume, influenced by BMI and sex, is strongly associated with grip strength, indicating clinical relevance for assessing hand function.
  • This technology enables efficient evaluation of muscle health, with potential applications in therapeutic planning and monitoring functional outcomes.