Int J Comput Assist Radiol Surg
March 2021
Purpose: To develop an automated segmentation approach for cochlear microstructures [scala tympani (ST), scala vestibuli (SV), modiolus (Mod), mid-modiolus (Mid-Mod), and round window membrane (RW)] in clinical cone beam computed tomography (CBCT) images of the temporal bone for use in surgical simulation software and for preoperative surgical evaluation.
Methods: This approach was developed using the publicly available OpenEar (OE) Library that includes temporal bone specimens with spatially registered CBCT and 3D micro-slicing images. Five of these datasets were spatially aligned to our internal OSU atlas.
Objective: Competency-based surgical training involves progressive autonomy given to the trainee. This requires systematic and evidence-based assessment with well-defined standards of proficiency. The objective of this study is to develop standards for the cross-institutional mastoidectomy assessment tool to inform decisions regarding whether a resident demonstrates sufficient skill to perform a mastoidectomy with or without supervision.
View Article and Find Full Text PDFHome healthcare workers (HHWs) are routinely exposed to occupational safety hazards when servicing patients in their homes that put them at risk for injury. These hazards can be broadly classified as "electric, fire and burn," "environmental," or "slip, trip, and lift" hazards. To better train HHWs regarding their potential exposure to these hazards, a home healthcare virtual simulation training system (HH-VSTS) was developed.
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
August 2019
Purpose: To develop a time-efficient automated segmentation approach that could identify surface structures on the temporal bone for use in surgical simulation software and preoperative surgical training.
Methods: An atlas-based segmentation approach was developed to segment the tegmen, sigmoid sulcus, exterior auditory canal, interior auditory canal, and posterior canal wall in normal temporal bone CT images. This approach was tested in images of 20 cadaver bones (10 left, 10 right).