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Related Concept Videos

The Spinal Cord01:54

The Spinal Cord

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The spinal cord is the body’s major nerve tract of the central nervous system, communicating afferent sensory information from the periphery to the brain and efferent motor information from the brain to the body. The human spinal cord extends from the hole at the base of the skull, or foramen magnum, to the level of the first or second lumbar vertebra.
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Spinal Cord01:26

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The spinal cord, a critical component of the central nervous system, extends from the base of the brainstem to the lumbar region of the vertebral column. It is essential for maintaining physical stability and facilitating communication between the brain and peripheral parts of the body.
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Spinal Nerves: Anatomy01:23

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Spinal nerves are pivotal conduits in the nervous system, bridging the central nervous system (CNS) with the peripheral nervous system (PNS). These nerves enable a complex communication network between the brain, spinal cord, and the rest of the body, facilitating sensory input, motor output, and autonomic functions.
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Spinal Cord: Cross-sectional Anatomy01:16

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The cross-sectional anatomy of the spinal cord offers a detailed view of its complex structure and function within the central nervous system. At the core of the spinal cord lies the gray matter, characterized by its butterfly or "H"-shaped appearance in cross-section. This central region is enveloped by white matter, with the overall structure divided into symmetrical halves by the dorsal median sulcus and the ventral median fissure.
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The spinal cord is an integral hub for motor and sensory information that enables the brain to communicate with the peripheral nervous system (PNS). This communication consists of relaying sensory data and transmission of motor commands.
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Spinal Cord: Gross Anatomy01:15

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The spinal cord resides within the protective confines of the vertebral column. It is the main pathway for information traveling between the brain and the body. It plays a fundamental role in nearly all bodily functions, from simple reflexes to complex motor movements. The spinal cord begins at the medulla oblongata at the base of the brainstem and extends downward, terminating at the conus medullaris near the first and second lumbar vertebrae. The spinal cord's length in adults is...
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Related Experiment Video

Updated: Sep 11, 2025

The Preparation of Oblique Spinal Cord Slices for Ventral Root Stimulation
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Automatic segmentation of the spinal cord nerve rootlets.

Jan Valošek1,2,3,4, Theo Mathieu1, Raphaëlle Schlienger5

  • 1NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.

Imaging Neuroscience (Cambridge, Mass.)
|August 13, 2025
PubMed
Summary
This summary is machine-generated.

An automated method accurately identifies spinal nerve rootlets in MRI scans, aiding spinal cord research. This technique shows stable performance across various imaging conditions and is available open-source.

Keywords:
deep learningmagnetic resonance imagingnerve rootletssegmentationspinal cord

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Last Updated: Sep 11, 2025

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Image Analysis

Background:

  • Precise identification of spinal nerve rootlets is crucial for mapping spinal cord functional activity.
  • Current methods for spinal level delineation can be labor-intensive and require expert knowledge.

Purpose of the Study:

  • To develop an automated semantic segmentation method for spinal nerve rootlets using T2-weighted MRI.
  • To evaluate the performance and generalizability of the automated method across different datasets and acquisition parameters.

Main Methods:

  • A 3D multi-class convolutional neural network was trained using an active learning approach on T2-weighted MRI scans.
  • The network was trained to segment C2-C8 dorsal nerve rootlets, with each output class representing a spinal level.
  • The method was validated on independent datasets to assess inter-site, inter-session, and inter-resolution variability.

Main Results:

  • The automated method achieved a mean Dice score of 0.67 ± 0.16, indicating good segmentation performance.
  • Low inter-vendor and inter-site variability (coefficient of variation ≤ 1.41%) and inter-session variability (coefficient of variation ≤ 1.30%) were observed.
  • The model demonstrated stable predictions across diverse MRI vendors, sites, and sessions.

Conclusions:

  • The proposed automated method provides accurate and robust semantic segmentation of spinal nerve rootlets.
  • This open-source tool, integrated into the Spinal Cord Toolbox, facilitates precise spinal level identification for research.
  • The method's stability across varying conditions supports its clinical and research applicability.