Pipeline for Automated Processing of Clinical Cone-Beam Computed Tomography for Patient-Specific Temporal Bone Simulation: Validation and Clinical Feasibility.

OBJECTIVE: Patient-specific simulation allows the surgeon to plan and rehearse the surgical approach ahead of time. Preoperative clinical imaging for this purpose requires time-consuming manual processing and segmentation of landmarks such as the facial nerve. We aimed to evaluate an automated pipeline with minimal manual interaction for processing clinical cone-beam computed tomography (CBCT) temporal bone imaging for patient-specific virtual reality (VR) simulation. STUDY DESIGN: Prospective image processing of retrospective imaging series. SETTING: Academic hospital. METHODS: Eleven CBCTs were selected based on quality and used for validation of the processing pipeline. A larger naturalistic sample of 36 CBCTs were obtained to explore parameters for successful processing and feasibility for patient-specific VR simulation.Visual inspection and quantitative metrics were used to validate the accuracy of automated segmentation compared with manual segmentation. Range of acceptable rotational offsets and translation point selection variability were determined. Finally, feasibility in relation to image acquisition quality, processing time, and suitability for VR simulation was evaluated. RESULTS: The performance of automated segmentation was acceptable compared with manual segmentation as reflected in the quantitative metrics. Total time for processing for new data sets was on average 8.3 minutes per data set; of this, it was less than 30 seconds for manual steps. Two of the 36 data sets failed because of extreme rotational offset, but overall the registration routine was robust to rotation and manual selection of a translational reference point. Another seven data sets had successful automated segmentation but insufficient suitability for VR simulation. CONCLUSION: Automated processing of CBCT imaging has potential for preoperative VR simulation but requires further refinement.

Automated Calculation of Cochlear Implant Electrode Insertion Parameters in Clinical Cone-Beam CT.

HYPOTHESIS: Automated processing of postoperative clinical cone-beam CT (CBCT) of cochlear implant (CI) patients can be used to accurately determine electrode contacts and integrated with an atlas-based mapping of cochlear microstructures to calculate modiolar distance, angular insertion distance, and scalar location of electrode contacts. BACKGROUND: Hearing outcomes after CI surgery are dependent on electrode placement. CBCT is increasingly used for in-office temporal bone imaging and might be routinely used for pre- and post-surgical evaluation. METHODS: Thirty-six matched pairs of pre- and postimplant CBCT scans were obtained. These were registered with an atlas to model cochlear microstructures in each dataset. Electrode contact center points were automatically determined using thresholding and electrode insertion parameters were calculated. Automated localization and calculation were compared with manual segmentation of contact center points as well as manufacturer specifications. RESULTS: Automated electrode contact detection aligned with manufacturer specifications of spacing and our algorithms worked for both distantly- and closely spaced arrays. The average difference between the manual and the automated selection was 0.15 mm, corresponding to a 1.875 voxel difference in each plane at the scan resolution. For each case, we determined modiolar distance, angular insertion depth, and scalar location. These calculations also resulted in similar insertion values using manual and automated contact points as well as aligning with electrode properties. CONCLUSION: Automated processing of implanted high-resolution CBCT images can provide the clinician with key information on electrode placement. This is one step toward routine use of clinical CBCT after CI surgery to inform and guide postoperative treatment.

Atlas-based segmentation of cochlear microstructures in cone beam CT.

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. An atlas of cochlear microstructures was created from one of the OE datasets. An affine registration of this atlas to the remaining OE CBCT images was used for automatically segmenting the cochlear microstructures. Quantitative metrics and visual review were used for validating the automatic segmentations. RESULTS: The average DICE metrics were 0.77 and 0.74 for the ST and SV, respectively. The average Hausdorff distance (AVG HD) was 0.11 mm and 0.12 mm for both scalae. The mean distance between the centroids for the round window was 0.32 mm, and the mean AVG HD was 0.09 mm. The mean distance and angular rotation between the mid-modiolar axes were 0.11 mm and 9.8 degrees, respectively. Visually, the segmented structures were accurate and similar to that manually traced by an expert observer. CONCLUSIONS: An atlas-based approach using 3D micro-slicing data and affine spatial registration in the cochlear region was successful in segmenting cochlear microstructures of temporal bone anatomy for use in simulation software and potentially for pre-surgical planning and rehearsal.

Segmentation of Temporal Bone Anatomy for Patient-Specific Virtual Reality Simulation.

OBJECTIVES: Virtual reality (VR) simulation for patient-specific pre-surgical planning and rehearsal requires accurate segmentation of key surgical landmark structures such as the facial nerve, ossicles, and cochlea. The aim of this study was to explore different approaches to segmentation of temporal bone surgical anatomy for patient-specific VR simulation. METHODS: De-identified, clinical computed tomography imaging of 9 pediatric patients aged 3 months to 12 years were obtained retrospectively. The patients represented normal anatomy and key structures were manually segmented using open source software. The OTOPLAN (CAScination AG, Bern, Switzerland) otological planning software was used for guided segmentation. An atlas-based algorithm was used for computerized, automated segmentation. Experience with the different approaches as well as time and resulting models were compared. RESULTS: Manual segmentation was time consuming but also the most flexible. The OTOPLAN software is not designed specifically for our purpose and therefore the number of structures that can be segmented is limited, there was some user-to-user variation as well as volume differences compared with manual segmentation. The atlas-based automated segmentation potentially allows a full range of structures to be segmented and produces segmentations comparable to those of manual segmentation with a processing time that is acceptable because of the minimal user interaction. CONCLUSION: Segmentation is fundamental for patient-specific VR simulation for pre-surgical planning and rehearsal in temporal bone surgery. The automated segmentation algorithm currently offers the most flexible and feasible approach and should be implemented. Further research is needed in relation to cases of abnormal anatomy. LEVEL OF EVIDENCE: 4.