Automatic analysis of global spinal alignment from simple annotation of vertebral bodies.
Automatic analysis of global spinal alignment from simple annotation of vertebral bodies.
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从椎体的简单注释自动分析全局脊柱对齐。
DOI：
10.1117/1.jmi.7.3.035001
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Siewerdsen,JeffreyH
中科院分区：
文献类型：
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作者：
Doerr,SophiaA;DeSilva,Tharindu;Vijayan,Rohan;Han,Runze;Uneri,Ali;Ketcha,MichaelD;Zhang,Xiaoxuan;Khanna,Nishanth;Westbroek,Erick;Jiang,Bowen;Zygourakis,Corinna;Aygun,Nafi;Theodore,Nicholas;Siewerdsen,JeffreyH
目的：全球脊柱对准（GSA）的测量是一个重要方面的诊断和治疗评价脊柱畸形，但受到高水平的阅片者之间的变异性。方法：提出了两种方法自动GSA测量，以减轻这种变异性，减少手动测量的负担。这两种方法都使用脊柱计算机断层扫描（CT）中的椎骨标签作为输入：第一种（EndSeg）使用输入标签作为种子点分割椎骨终板;第二种（SpNorm）计算输入标签的二维曲线拟合。进行研究，以表征EndSeg和SpNorm的性能相比，手动GSA测量由五名临床医生，包括近端胸椎后凸，主要胸椎后凸，腰椎前凸的测量结果：对于自动方法，93.8%的终板角度估计值在阅片者间95%的置信区间（CI 95）。自动方法的所有GSA测量值均在读片员间CI 95范围内，自动和手动方法之间无统计学显著差异。SpNorm方法似乎特别强大，因为它没有segmentation.Conclusions操作：这样的方法可以提高GSA测量的再现性和可靠性，并可能适用于大规模的应用，例如，用于手术数据科学的结果评估。
Purpose:Measurement of global spinal alignment (GSA) is an important aspect of diagnosis and treatment evaluation for spinal deformity but is subject to a high level of inter-reader variability.Approach:Two methods for automatic GSA measurement are proposed to mitigate such variability and reduce the burden of manual measurements. Both approaches use vertebral labels in spine computed tomography (CT) as input: the first (EndSeg) segments vertebral endplates using input labels as seed points; and the second (SpNorm) computes a two-dimensional curvilinear fit to the input labels. Studies were performed to characterize the performance of EndSeg and SpNorm in comparison to manual GSA measurement by five clinicians, including measurements of proximal thoracic kyphosis, main thoracic kyphosis, and lumbar lordosis.Results:For the automatic methods, 93.8% of endplate angle estimates were within the inter-reader 95% confidence interval (CI95). All GSA measurements for the automatic methods were within the inter-reader CI95, and there was no statistically significant difference between automatic and manual methods. The SpNorm method appears particularly robust as it operates without segmentation.Conclusions:Such methods could improve the reproducibility and reliability of GSA measurements and are potentially suitable to applications in large datasets—e.g., for outcome assessment in surgical data science.