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Related Concept Videos

Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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DNA probes are fragments of DNA labeled with a reporter tag to enable their detection or purification. The resulting labeled DNA probes can then hybridize to target nucleic acid sequences through complementary base-pairing, and may be used to recover or identify these regions.
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The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
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Groupwise segmentation with multi-atlas joint label fusion.

Hongzhi Wang1, Paul A Yushkevich1

  • 1Department of Radiology, University of Pennsylvania, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|February 8, 2014
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Summary

Groupwise segmentation improves multi-atlas label fusion by ensuring consistent segmentations across images. This novel approach explicitly addresses label propagation errors for enhanced accuracy in medical image analysis.

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Area of Science:

  • Medical image analysis
  • Computational anatomy

Background:

  • Simultaneous segmentation of image sets (groupwise segmentation) enhances consistency for structures of interest compared to individual image segmentation.
  • Multi-atlas label fusion is a common technique for medical image segmentation.

Purpose of the Study:

  • To enhance multi-atlas label fusion performance by integrating a groupwise segmentation framework.
  • To introduce a novel statistical model for groupwise multi-atlas label fusion.

Main Methods:

  • Development of a novel statistical model extending the groupwise segmentation framework.
  • Joint label fusion framework that explicitly accounts for errors during label propagation.

Main Results:

  • Demonstrated effectiveness of the groupwise segmentation technique for hippocampus segmentation.
  • Improved segmentation accuracy compared to traditional methods through explicit error handling.

Conclusions:

  • The proposed groupwise segmentation framework significantly enhances multi-atlas label fusion.
  • Explicitly addressing label propagation errors is crucial for improving segmentation consistency and accuracy.