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Related Concept Videos

Regional Terms01:12

Regional Terms

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Regional terms describe anatomy by dividing the body parts into different regions that contain structures involved in contributing similar functions. Using these terms helps increase the accurate description and identification of the particular region of interest or region affected by the disease.
Primarily, the human body has two major regions, the axial and appendicular regions. The axial region comprises regions from the head to the abdomen and makes up the central body axis. In contrast,...
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To promote clear communication, for instance, about the location of a patient's abdominal pain or a suspicious mass, anatomists and clinicians typically use imaginary lines to categorize the abdominopelvic cavity into either four quadrants or nine regions to identify organs in the cavity.
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Vertebral Column: Regions and Curvature01:16

Vertebral Column: Regions and Curvature

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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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Classification of Bones01:18

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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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Anatomy of the Brain: Major Regions01:20

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The brain is the most complex organ in the human body. It consists of four main parts: the cerebrum, diencephalon, cerebellum, and brainstem.
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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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Related Experiment Video

Updated: Oct 16, 2025

Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts
07:56

Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts

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The lumbar region localization using bone anatomy feature graphs.

Shuang Ma1,2, Jinzhu Yang3,4, Qi Sun1,2

  • 1Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.

Medical & Biological Engineering & Computing
|October 16, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for automatically locating the lumbar spine using bone anatomy feature graphs. The approach accurately identifies lumbar vertebrae boundaries, aiding in disease diagnosis and surgical planning.

Keywords:
Bone anatomyFeature graphsInferior boundary of L5 or L6Lumbar region localizationSuperior boundary of L1

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Area of Science:

  • Medical Imaging
  • Anatomy
  • Computer-Aided Diagnosis

Background:

  • Accurate lumbar region localization is crucial for diagnosing spinal diseases, studying morphology, and surgical planning.
  • Existing methods face challenges due to diverse pathologies and irregular lumbar vertebrae counts (e.g., L6).

Purpose of the Study:

  • To develop a novel and robust method for automatic lumbar region localization.
  • To address challenges posed by anatomical variations and pathologies in lumbar spine imaging.

Main Methods:

  • Proposed a novel lumbar region localization method utilizing bone anatomy feature graphs.
  • Introduced two feature graphs: LS for L5/L6 inferior boundary and TL for L1 superior boundary.
  • Validated the method on public (xVertSeg) and private (197 CT scans) datasets.

Main Results:

  • Achieved high localization accuracy with Dice and Jaccard coefficients of 98.09 ± 0.84% and 96.27 ± 1.62%, respectively.
  • Demonstrated robustness across various conditions including normal, scoliosis, deformity, hyperosteogeny, 6-vertebrae, and implant scans.

Conclusions:

  • The proposed bone anatomy feature graph method provides accurate and robust automatic lumbar region localization.
  • This technique has significant potential for improving clinical applications in lumbar spine diagnosis and surgery.