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Related Experiment Video

Updated: Jun 26, 2025

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BounTI (boundary-preserving threshold iteration): A user-friendly tool for automatic hard tissue segmentation.

Marius Didziokas1, Erwin Pauws2, Lars Kölby3

  • 1Department of Mechanical Engineering, University College London, London, UK.

Journal of Anatomy
|May 18, 2024
PubMed
Summary

A new algorithm, BounTI, automates the segmentation of calcified tissues from X-ray Computed Tomography (CT) images. This user-friendly tool effectively segments craniofacial bones across various species and scan qualities, aiding anatomical research.

Keywords:
3D reconstructionbonecomputed tomographycraniofacial systemcraniosynostosisimage processingskull

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Area of Science:

  • Biomedical Imaging
  • Computational Biology
  • Anatomy

Background:

  • 3D reconstruction from X-ray Computed Tomography (CT) images requires segmentation, which is often manual and time-consuming for complex biological structures.
  • Existing automated segmentation tools have limitations, particularly for multipart segmentation, leaving a gap in efficient image analysis for biological sciences.

Purpose of the Study:

  • To develop an open-access, user-friendly tool for automatic segmentation of calcified tissues, with a specific focus on craniofacial bones.
  • To introduce BounTI, a novel segmentation algorithm designed to preserve boundaries between separate segments through iterative thresholding.

Main Methods:

  • Developed BounTI, an iterative thresholding algorithm for automated segmentation of hard tissues.
  • Investigated the impact of input parameters on algorithm performance.
  • Tested the algorithm's versatility on CT images of craniofacial systems from diverse species (snake, lizard, amphibian, mouse, human) and varying scan qualities.

Main Results:

  • BounTI effectively and automatically segmented craniofacial systems across a range of species.
  • High-resolution microCT images yielded more accurate segmentation, but the algorithm also successfully segmented lower-quality clinical images.
  • Manual intervention methods were integrated for cases with insufficient scan quality.

Conclusions:

  • BounTI is a versatile, user-friendly tool for automatic hard tissue segmentation, applicable beyond craniofacial anatomy.
  • The algorithm's accessibility as an Avizo/Amira add-on, standalone executable, and Python library benefits the anatomical research community.
  • BounTI addresses the need for efficient, automated segmentation in biological and anatomical studies.