A statistical shape model of the human second cervical vertebra.

Area of Science:

• Medical Imaging
• Biomedical Engineering
• Computational Anatomy

Background:

• Statistical shape models (SSMs) are crucial for efficient 3D model development and segmentation.
• The second cervical vertebra (C2) presents morphological complexity and population variability, posing challenges for model creation.

Purpose of the Study:

• To describe the generation of a statistical shape model (SSM) for the second cervical vertebra (C2).
• To provide the C2 SSM for public use by the scientific community.
• To address the challenges of C2 morphological complexity and variability.

Main Methods:

• Utilized manually segmented anonymized patient CT scans.
• Employed Procrustes alignment for surface model registration.
• Applied Gaussian process and Principal Component Analysis (PCA) for model fitting and variability inclusion.

Main Results:

• Generated an SSM using 92 CT scans.
• Evaluated the SSM for specificity, compactness, and generalization.
• Made the C2 SSM freely available in Slicer with a visualization module.

Conclusions:

• The developed SSM effectively represents C2 shape variability.
• The SSM facilitates semi-automatic segmentation and 3D model generation.
• This tool is expected to significantly benefit surgical planning.