Abstract
Background Most existing objective surgical motion analysis schemes are limited to structured
surgical tasks or recognition of motion patterns for certain categories of surgeries.
Analyzing instrument motion data with respect to anatomical structures can break the
limit, and an anatomical region segmentation algorithm is required for the analysis.
Methods An atlas was generated by manually segmenting the skull base into nine regions, including
left/right anterior/posterior ethmoid sinuses, frontal sinus, left and right maxillary
sinuses, nasal airway, and sphenoid sinus. These regions were selected based on anatomical
and surgical significance in skull base and sinus surgery. Six features, including
left and right eye center, nasofrontal beak, anterior tip of nasal spine, posterior
edge of hard palate at midline, and clival body at foramen magnum, were used for alignment.
The B-spline deformable registration was adapted to fine tune the registration, and
bony boundaries were automatically extracted for final precision improvement. The
resultant deformation field was applied to the atlas, and the motion data were clustered
according to the deformed atlas.
Results Eight maxillofacial computed tomography scans were used in experiments. One was manually
segmented as the atlas. The others were segmented by the proposed method. Motion data
were clustered into nine groups for every dataset and outliers were filtered.
Conclusions The proposed algorithm improved the efficiency of motion data clustering and requires
limited human interaction in the process. The anatomical region segmentations effectively
filtered out the portion of motion data that are out of surgery sites and grouped
them according to anatomical similarities.
Keywords
anatomical region - atlas-based segmentation - motion analysis - objective skill assessment
- operating room data - skull base - sinus surgery
