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Related Concept Videos

Somatosensation01:33

Somatosensation

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The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.
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In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
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Sampling materials are classified into three main types: solid, liquid, and gas.
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Touching Soma Segmentation Based on the Rayburst Sampling Algorithm.

Tianyu Hu1,2, Qiufeng Xu1,2, Wei Lv1,2

  • 1Britton Chance Center for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.

Neuroinformatics
|September 24, 2017
PubMed
Summary
This summary is machine-generated.

Accurate neuronal soma segmentation is crucial for brain analysis. This study introduces a fast, automated method combining Rayburst sampling and ellipsoid fitting, achieving high accuracy comparable to manual segmentation.

Keywords:
Distance transformImage analysisRayburst sampling algorithmSoma segmentation

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Area of Science:

  • Neuroscience
  • Biomedical Imaging
  • Computational Biology

Background:

  • Neuronal soma segmentation is vital for quantifying neuron morphology.
  • High-throughput analysis is hindered by manual segmentation's time demands.
  • Automatic segmentation struggles with touching neuronal somas.

Purpose of the Study:

  • To develop an automated method for accurate neuronal soma segmentation.
  • To address the challenge of segmenting touching neuronal somas.
  • To enable high-throughput analysis of neuronal morphology.

Main Methods:

  • A novel method combining Rayburst sampling and ellipsoid fitting was proposed.
  • The Rayburst sampling algorithm was improved for soma surface detection.
  • Ellipsoid fitting refined sampled surfaces into smooth shapes for analysis.

Main Results:

  • The proposed method achieved accurate soma segmentation.
  • Performance was comparable to manual segmentation (gold standard).
  • The method demonstrated high-speed processing capabilities.

Conclusions:

  • The combined Rayburst sampling and ellipsoid fitting method offers accurate and efficient neuronal soma segmentation.
  • This approach overcomes limitations of existing automatic methods for touching somas.
  • The method shows potential for application to large-scale neuroimaging datasets.