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Abstract：
OBJECTIVE: To establish an analysis pipeline for the volumetric evaluation of the osteotomy site after bilateral sagittal split osteotomy (BSSO).
METHODS: Cone-beam computed tomography (CBCT) was performed before, directly after BSSO, and 6-12 months after surgery. Image segmentations of each osteotomy gap data set were performed manually by four physicians and were compared to a semi-automatic segmentation approach.
RESULTS: Five patients with a total of ten osteotomy gaps were included. The mean interclass correlation coefficient (ICC) of individual patients was 0.782 and the standard deviation 0.080 when using the manual segmentation approach. However, the mean ICC of the evaluation of anatomical sites and time points separately was 0.214, suggesting a large range of deviation within the manual segmentation of each rater. The standard deviation was 0.355, further highlighting the extent of the variation. In contrast, the semi-automatic approach had a mean ICC of 0.491 and a standard deviation of 0.365, which suggests a relatively higher agreement among the operators compared to the manual segmentation approach. Furthermore, the volume of the osteotomy gap in the semi-automatic approach showed the same tendency in every site as the manual segmentation approach, but with less deviation.
CONCLUSIONS: The semi-automatic approach developed in the present study proved to be valid as a standardised method with high repeatability. Such image analysis methods could help to quantify the progression of bone healing after BSSO and beyond, eventually facilitating the earlier identification of patients with retarded healing.
METHODS: Cone-beam computed tomography (CBCT) was performed before, directly after BSSO, and 6-12 months after surgery. Image segmentations of each osteotomy gap data set were performed manually by four physicians and were compared to a semi-automatic segmentation approach.
RESULTS: Five patients with a total of ten osteotomy gaps were included. The mean interclass correlation coefficient (ICC) of individual patients was 0.782 and the standard deviation 0.080 when using the manual segmentation approach. However, the mean ICC of the evaluation of anatomical sites and time points separately was 0.214, suggesting a large range of deviation within the manual segmentation of each rater. The standard deviation was 0.355, further highlighting the extent of the variation. In contrast, the semi-automatic approach had a mean ICC of 0.491 and a standard deviation of 0.365, which suggests a relatively higher agreement among the operators compared to the manual segmentation approach. Furthermore, the volume of the osteotomy gap in the semi-automatic approach showed the same tendency in every site as the manual segmentation approach, but with less deviation.
CONCLUSIONS: The semi-automatic approach developed in the present study proved to be valid as a standardised method with high repeatability. Such image analysis methods could help to quantify the progression of bone healing after BSSO and beyond, eventually facilitating the earlier identification of patients with retarded healing.
摘要：
目的：建立用于双侧矢状面劈开截骨（BSSO）后截骨部位体积评估的分析管道。
方法：之前进行了锥形束计算机断层扫描（CBCT），紧接在BSSO之后，手术后6-12个月。每个截骨间隙数据集的图像分割由四名医生手动执行，并与半自动分割方法进行比较。
结果：纳入5例患者，共10个截骨间隙。使用手动分割方法时，单个患者的平均类间相关系数（ICC）为0.782，标准偏差为0.080。然而,解剖部位和时间点评估的平均ICC分别为0.214,提示在每个评分者的人工分割中存在较大的偏差.标准偏差为0.355,进一步突出了变化的程度。相比之下,半自动分割方法的平均ICC为0.491,标准偏差为0.365,这表明与手动分割方法相比,操作者之间的一致性相对较高.此外，半自动方法中截骨间隙的体积在每个部位都显示出与手动分割方法相同的趋势，但偏差较小。
结论：本研究中开发的半自动方法被证明是有效的标准化方法，具有高重复性。这种图像分析方法可以帮助量化BSSO及以后的骨愈合进展，最终有助于早期识别愈合迟缓的患者。
方法：之前进行了锥形束计算机断层扫描（CBCT），紧接在BSSO之后，手术后6-12个月。每个截骨间隙数据集的图像分割由四名医生手动执行，并与半自动分割方法进行比较。
结果：纳入5例患者，共10个截骨间隙。使用手动分割方法时，单个患者的平均类间相关系数（ICC）为0.782，标准偏差为0.080。然而,解剖部位和时间点评估的平均ICC分别为0.214,提示在每个评分者的人工分割中存在较大的偏差.标准偏差为0.355,进一步突出了变化的程度。相比之下,半自动分割方法的平均ICC为0.491,标准偏差为0.365,这表明与手动分割方法相比,操作者之间的一致性相对较高.此外，半自动方法中截骨间隙的体积在每个部位都显示出与手动分割方法相同的趋势，但偏差较小。
结论：本研究中开发的半自动方法被证明是有效的标准化方法，具有高重复性。这种图像分析方法可以帮助量化BSSO及以后的骨愈合进展，最终有助于早期识别愈合迟缓的患者。
