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J Neurol Surg B Skull Base 2017; 78(06): 490-496
DOI: 10.1055/s-0037-1604406
Original Article
Georg Thieme Verlag KG Stuttgart · New York

Anatomical Region Segmentation for Objective Surgical Skill Assessment with Operating Room Motion Data

Yangming Li
1   Department of Electrical Engineering, University of Washington, Seattle, Washington, United States
,
Randall A. Bly
2   Department of Otolaryngology-Head and Neck Surgery, University of Washington School of Medicine, Seattle, Washington, United States
3   Seattle Children's Hospital, University of Washington School of Medicine, Seattle, Washington, United States
,
R. Alex Harbison
2   Department of Otolaryngology-Head and Neck Surgery, University of Washington School of Medicine, Seattle, Washington, United States
,
Ian M. Humphreys
2   Department of Otolaryngology-Head and Neck Surgery, University of Washington School of Medicine, Seattle, Washington, United States
,
Mark E. Whipple
2   Department of Otolaryngology-Head and Neck Surgery, University of Washington School of Medicine, Seattle, Washington, United States
,
Blake Hannaford
1   Department of Electrical Engineering, University of Washington, Seattle, Washington, United States
,
Kris S. Moe
2   Department of Otolaryngology-Head and Neck Surgery, University of Washington School of Medicine, Seattle, Washington, United States
› Author Affiliations
Further Information

Publication History

18 February 2017

10 June 2017

Publication Date:
31 July 2017 (online)

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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
 

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