An optimized process flow for rapid segmentation of cortical bones of the craniofacial skeleton using the level-set...

Area of Science:

• Medical Imaging
• Biomedical Engineering
• Computational Anatomy

Background:

• Accurate segmentation of craniofacial skeleton (CFS) cortical bone is crucial for quantitative analysis, patient-specific implant design, and image-guided surgery.
• The complex, thin morphology of CFS bone presents significant challenges for precise 3D segmentation from CT data.

Purpose of the Study:

• To develop and validate an optimized, automated processing pipeline for segmenting the 3D geometry of thin cortical bone structures within the CFS from CT images.
• To enhance the accuracy of bony segmentation by isolating and defining boundaries between cortical and trabecular bone in the craniofacial region.

Main Methods:

• Employed anoisotropic filtering and connected components for boundary enhancement between cortical and trabecular bone.
• Utilized boundary-tracking level-set methods, with parameters optimized via large-scale sensitivity analysis, to exploit the shell-like nature of cortical bone.
• Applied the developed segmentation process to clinical CT images from cadaveric CFS specimens.

Main Results:

• Achieved volumetric concurrencies of 0.904 and 0.719 for full CFS segmentations compared to manual segmentations.
• Demonstrated improved accuracy in the maxillary region, with concurrencies reaching 0.936 and 0.846.
• The automated method successfully segmented both the cortical shell and trabecular boundaries of the CFS.

Conclusions:

• The presented highly automated approach effectively segments craniofacial cortical bone and trabecular boundaries in clinical CT images.
• Segmentation accuracy may be influenced by initial CT scan resolution and the contrast between cortical and trabecular bone.
• Further validation on larger datasets is needed to assess performance across varying image quality and CFS morphologies.