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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

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 Cord: Information Processing01:10

Spinal Cord: Information Processing

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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.
Sensory Information Processing
Sensory information processing begins at the sensory receptors located in the skin and other tissues, which detect somatic sensory stimuli such as touch, temperature, or pain. These receptors function as catalysts, initiating...
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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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Convolution Properties II01:17

Convolution Properties II

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The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
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Classifying Matter by State02:49

Classifying Matter by State

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Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
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Acute and Chronic Tactile Sensory Testing after Spinal Cord Injury in Rats
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Spinal cord gray matter segmentation using deep dilated convolutions.

Christian S Perone1, Evan Calabrese2,3, Julien Cohen-Adad4,5

  • 1NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, H3T 1J4, Canada.

Scientific Reports
|April 15, 2018
PubMed
Summary
This summary is machine-generated.

We developed a new deep learning method for automatically segmenting human spinal cord gray matter (GM) in MRI scans. This advanced technique achieves state-of-the-art results, improving disability biomarker analysis for neurological disorders.

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Deep Learning

Background:

  • Gray matter (GM) changes are linked to neurological disorders and serve as disability biomarkers in amyotrophic lateral sclerosis.
  • Accurate segmentation of spinal cord GM is crucial for research and clinical applications.

Purpose of the Study:

  • To develop a fully-automated, deep learning-based method for segmenting human spinal cord gray matter.
  • To validate the method's performance on both in vivo and ex vivo MRI data.

Main Methods:

  • A novel, end-to-end deep learning model was designed for automated spinal cord GM segmentation.
  • The method was evaluated against six other segmentation techniques in a comparative challenge.

Main Results:

  • The proposed method achieved state-of-the-art performance, excelling in 8 out of 10 evaluation metrics.
  • Significant reduction in network parameters was observed compared to traditional architectures like U-Nets.

Conclusions:

  • The developed deep learning approach offers a simple, efficient, and highly accurate solution for spinal cord GM segmentation.
  • This method has the potential to advance the study of neurological disorders through improved biomarker analysis.