BounTI (boundary-preserving threshold iteration)

BounTI implements iterative thresholding to preserve boundaries between separate segments during automatic segmentation of calcified tissues.

Key Features:

• Boundary-preserving segmentation: Iterative thresholding preserves boundaries between separate segments during automatic segmentation.
• Multipart segmentation capability: Automates segmentation of complex structures composed of several sub-parts that remain largely manual in other tools.
• Manual intervention methods: Methods for manual intervention are included when scan quality is insufficient to achieve desired segmentation results.
• Multiple implementations: Available as an Avizo/Amira add-on, a stand-alone Windows executable, and a Python library.

Scientific Applications:

• Craniofacial bone segmentation: Automatic segmentation of craniofacial bones from X-ray Computed Tomography (CT) images across diverse species including snake, lizard, amphibian, mouse, and human skull.
• Hard tissue segmentation: General application to automatic segmentation of any hard tissue beyond craniofacial systems.
• Cross-species anatomical research: Versatile segmentation of craniofacial systems from different species with various scan qualities, from high-resolution microCT to significantly lower-quality clinical images.

Methodology:

BounTI uses iterative thresholding to preserve boundaries between separate segments. The algorithm was tested on CT images of the craniofacial system from different species with various scan qualities to investigate the effect of several input parameters on its outcome.