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Related Experiment Video

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Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
09:36

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Published on: March 14, 2018

Variation in segmentation of bone from micro-CT imaging: implications for quantitative morphometric analysis.

I H Parkinson1, A Badiei, N L Fazzalari

  • 1Bone and Joint Research Laboratory, Division of Tissue Pathology, Institute of Medical and Veterinary Science and Hanson Institute, Adelaide, Australia. ian.parkinson@imvs.sa.gov.au

Australasian Physical & Engineering Sciences in Medicine
|August 14, 2008
PubMed
Summary
This summary is machine-generated.

Manual bone segmentation in micro-CT imaging shows high variability, impacting morphometric analysis accuracy. Automated methods like Otsu's algorithm offer a more consistent alternative for reliable results.

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Area of Science:

  • Biomedical Engineering
  • Orthopedic Research
  • Medical Imaging Analysis

Background:

  • Accurate morphometric analysis of bone structure is crucial for understanding skeletal diseases and treatment efficacy.
  • Micro-computed tomography (micro-CT) is a key technology for high-resolution bone imaging.
  • Image segmentation is a critical preprocessing step in micro-CT analysis, directly influencing measurement accuracy.

Purpose of the Study:

  • To quantify the variability in cancellous bone segmentation performed by multiple human operators.
  • To compare the consistency of manual segmentation with automated segmentation using Otsu's algorithm.
  • To assess the impact of segmentation variability on key bone morphometric parameters.

Main Methods:

  • Cancellous bone cubes from human vertebral bodies were imaged using micro-CT.
  • Three operators independently determined global thresholds for image segmentation.
  • Automated segmentation was performed using Otsu's algorithm.
  • Bone volume fraction, trabecular thickness, separation, number, bone pattern factor, structure model index, and degree of anisotropy were calculated.
  • Percent bias and percent random error were computed between manual and automated methods.

Main Results:

  • Manual segmentation exhibited significant variability, with maximum percent bias up to 22.0% for bone volume fraction (BV/TV) and 13.1% for trabecular thickness (Tb.Th).
  • Random errors in manual segmentation reached up to 11.3% for BV/TV and 6.4% for Tb.Th.
  • Differences between operators could exceed experimental variations, potentially leading to misinterpretation of cancellous bone structure results.

Conclusions:

  • Manual image segmentation in micro-CT analysis is highly sensitive to operator-dependent parameters.
  • Operator variability can introduce substantial errors in morphometric measurements, potentially greater than experimental differences.
  • Automated segmentation methods, such as Otsu's algorithm, are recommended for improved consistency and reliability in micro-CT bone analysis.