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Related Concept Videos

General Structure of a Vertebra01:30

General Structure of a Vertebra

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A typical vertebra, with the exception of the sacrum and coccyx, consists of a body, a vertebral arch, and seven different projections termed processes. The anterior portion of the vertebrae, the body, supports about half the body’s weight. The vertebral bodies progressively increase in size and thickness from the cervical region to the lumbar region of the vertebral column. The intervertebral discs present between the bodies of adjacent vertebrae firmly unites them, forming a continuous...
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The vertebral column or spine is a flexible column that supports the head, neck, and body and  allows for their movements. It also protects the spinal cord.
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The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
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The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
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In addition to being held together by the intervertebral discs, adjacent vertebrae also articulate with each other at synovial joints formed between the superior and inferior articular processes called zygapophysial joints (facet joints). These are plane joints that provide for only limited motions between the vertebrae. The orientation of the articular processes at these joints varies in different regions of the vertebral column and serves to determine the types of motions available in each...
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Multi-modal vertebrae recognition using Transformed Deep Convolution Network.

Yunliang Cai1, Mark Landis2, David T Laidley2

  • 1Dept. of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, 1151 Richmond St, London, ON, Canada.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|April 23, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new deep learning method for automatic vertebra recognition across MRI and CT scans. The Transformed Deep Convolution Network (TDCN) enhances accuracy in identifying vertebra location, name, and pose, aiding spinal diagnostics.

Keywords:
Convolution networkDeep learningVertebra detectionVertebra recognition

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Radiology
  • Spinal Diagnostics

Background:

  • Automatic vertebra recognition is crucial for spinal clinical diagnoses, yet challenging due to variations in medical imaging data (MR/CT).
  • Existing methods struggle with diverse appearances, shapes, and poses of vertebrae across different imaging modalities.

Purpose of the Study:

  • To develop a robust method for multi-modal vertebra recognition, addressing challenges in MR/CT image variations.
  • To enable fully automatic identification of vertebra location, naming, and pose for clinical applications.

Main Methods:

  • Proposed a novel deep learning architecture: Transformed Deep Convolution Network (TDCN).
  • TDCN unsupervisely fuses image features from different modalities (MR and CT).
  • The architecture naturally incorporates feature fusion and automatic pose rectification within a multi-layer network.

Main Results:

  • The fusion of MR and CT features improved discriminative representation and invariance of vertebra patterns.
  • The method demonstrated superior performance compared to existing detection methods.
  • Achieved fully automatic location, naming, and pose recognition of vertebrae.

Conclusions:

  • The Transformed Deep Convolution Network (TDCN) offers a powerful solution for multi-modal vertebra recognition.
  • This method enhances the automatic processing of diverse spinal imaging data, improving clinical diagnostic efficiency.
  • The TDCN facilitates routine clinical practice by providing comprehensive and automatic vertebra analysis.