Pipeline for Automated Processing of Clinical Cone-Beam Computed Tomography for Patient-Specific Temporal Bone Simulation: Validation and Clinical Feasibility.
| Autor: | Andersen SAW, Hittle B; Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio., Keith JP; Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio., Powell KA; Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio., Wiet GJ |
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| Jazyk: | angličtina |
| Zdroj: | Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology [Otol Neurotol] 2023 Feb 01; Vol. 44 (2), pp. e88-e94. Date of Electronic Publication: 2022 Nov 29. |
| DOI: | 10.1097/MAO.0000000000003771 |
| Abstrakt: | 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. Competing Interests: The authors disclose no conflicts of interest. (Copyright © 2022, Otology & Neurotology, Inc.) |
| Databáze: | MEDLINE |
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