Andersen Steven Arild Wuyts, Hittle Brad, Keith Jason P, Powell Kimerly A, Wiet Gregory J
Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio.
Otol Neurotol. 2023 Feb 1;44(2):e88-e94. doi: 10.1097/MAO.0000000000003771. Epub 2022 Nov 29.
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.
Prospective image processing of retrospective imaging series.
Academic hospital.
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.
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.
Automated processing of CBCT imaging has potential for preoperative VR simulation but requires further refinement.
针对特定患者的模拟使外科医生能够提前规划和演练手术入路。为此目的进行的术前临床成像需要对诸如面神经等标志进行耗时的手动处理和分割。我们旨在评估一种自动化流程，该流程只需最少的人工干预，即可处理临床锥形束计算机断层扫描（CBCT）颞骨成像，以进行针对特定患者的虚拟现实（VR）模拟。
对回顾性成像系列进行前瞻性图像处理。
学术医院。
根据质量选择了11例CBCT用于验证处理流程。获取了36例CBCT的更大自然样本，以探索成功处理的参数以及针对特定患者的VR模拟的可行性。与手动分割相比，使用视觉检查和定量指标来验证自动分割的准确性。确定了可接受的旋转偏移范围和平移点选择变异性。最后，评估了在图像采集质量、处理时间和VR模拟适用性方面的可行性。
如定量指标所示，与手动分割相比，自动分割的性能是可接受的。新数据集的总处理时间平均为每个数据集8.3分钟；其中，人工步骤的时间不到30秒。36个数据集中有2个因极端旋转偏移而失败，但总体而言，配准程序对旋转和平移参考点的手动选择具有鲁棒性。另外7个数据集自动分割成功，但对VR模拟的适用性不足。
CBCT成像的自动处理在术前VR模拟方面具有潜力，但需要进一步完善。
