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Related Concept Videos

Spinal Cord01:26

Spinal Cord

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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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The Spinal Cord01:54

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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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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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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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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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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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Related Experiment Video

Updated: Feb 16, 2026

Contrast Enhanced Ultrasound Imaging for Assessment of Spinal Cord Blood Flow in Experimental Spinal Cord Injury
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Automatic spinal cord localization, robust to MRI contrasts using global curve optimization.

Charley Gros1, Benjamin De Leener1, Sara M Dupont1

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

Medical Image Analysis
|December 31, 2017
PubMed
Summary
This summary is machine-generated.

A new automated method, OptiC, accurately detects the spinal cord centerline in MRI scans, improving quantitative analysis for neurological diseases. This robust tool achieves high coverage and low error, outperforming existing techniques, especially in complex cases.

Keywords:
DetectionGlobal optimizationMRIMachine learningSegmentationSpinal cord

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Area of Science:

  • Medical Imaging
  • Neuroscience
  • Biomedical Engineering

Background:

  • Magnetic Resonance Imaging (MRI) is crucial for spinal cord morphometry, aiding in disease diagnosis.
  • Current automatic spinal cord MRI processing tools lag behind brain imaging counterparts.
  • Developing automated, unbiased methods is essential for analyzing large spinal cord MRI datasets.

Purpose of the Study:

  • Introduce a fully automated, robust, and fast method for spinal cord centerline detection in MRI volumes.
  • Address the challenge of automatic spinal cord detection across diverse MRI contrasts, resolutions, and pathologies.
  • Enable reliable quantitative analysis of spinal cord morphometry, particularly in neurological diseases.

Main Methods:

  • Developed "OptiC", a novel algorithm using global optimization for spinal cord centerline detection.
  • Incorporated a probabilistic localization map and spatial consistency for rostro-caudal continuity.
  • Introduced a post-processing feature to automatically separate brain and spine regions, ensuring field-of-view independence.

Main Results:

  • OptiC achieved 98.77% coverage of the gold-standard spinal cord centerline with a 1.02 mm mean square error.
  • Demonstrated superior performance over Hough transform methods, especially in pathological cases (1.08 mm vs. 13.16 mm MSE).
  • Accurately identified brain regions (99% precision) and separated brain/spine regions with a 9.37 mm error.

Conclusions:

  • OptiC offers a reliable and efficient solution for automated spinal cord centerline detection in MRI.
  • The method's robustness in pathological cases facilitates more accurate quantitative analysis.
  • OptiC has the potential to significantly advance spinal cord morphometry studies in clinical research.