Detection of vertebral fractures in DXA VFA images using statistical models of appearance and a semi-automatic segmentation
Detection of vertebral fractures in DXA VFA images using statistical models of appearance and a semi-automatic segmentation
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DOI：
10.1007/s00198-009-1169-6
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发表时间：
2010-12-01
影响因子：
4
通讯作者：
Adams, J. E.
中科院分区：
文献类型：
--
作者：
Roberts, M. G.;Pacheco, E. M. B.;Adams, J. E.
脊椎骨折的形态测量诊断缺乏特异性。我们使用详细的形状和图像纹理模型参数，以提高定量裂缝识别的特异性。两名放射科医生对所有椎骨进行视觉分类，以进行系统培训和评价。椎体终板位于半自动分割方法，以获得分类inputs.Introduction椎体骨折是常见的骨质疏松性骨折，但目前的定量检测方法（形态学）缺乏特异性。我们使用详细的形状和纹理信息，开发更具体的定量分类器的脊椎骨折，以提高客观的脊椎骨折诊断。这些分类器需要一个详细的分割的椎骨终板，所以我们研究了使用半自动分割方法的一部分，diagnosis.Methods在一个训练集的360双能X射线吸收图像的椎骨手动分割。使用外观模型对椎骨的形状和图像纹理进行统计建模。两名放射科医生对椎骨进行了金标准分类。线性判别分类器检测骨折的椎骨外观模型参数进行训练。分类器的性能进行了评估，通过交叉验证手动和半自动分割，后者来自使用主动外观模型（AAM）。结果在95%灵敏度下，人工分割的假阳性率（FPR）分别为：5%（外观）和18%（形态测量）。半自动分割的灵敏度为：88%（外观）和79%（形态测量）。结论使用基于外观的分类器相比，标准的身高比形态测量的特异性和灵敏度提高。由于分割错误，与专家注释相比，使用半自动（AAM）分割时发生7%的总体灵敏度损失（95%特异性）。然而，分类器的灵敏度仍然是足够的计算机辅助诊断系统的脊椎骨折，特别是如果使用的分诊方法。
Morphometric methods of vertebral fracture diagnosis lack specificity. We used detailed shape and image texture model parameters to improve the specificity of quantitative fracture identification. Two radiologists visually classified all vertebrae for system training and evaluation. The vertebral endplates were located by a semiautomatic segmentation method to obtain classifier inputs.Introduction Vertebral fractures are common osteoporotic fractures, but current quantitative detection methods (morphometry) lack specificity. We used detailed shape and texture information to develop more specific quantitative classifiers of vertebral fracture to improve the objectivity of vertebral fracture diagnosis. These classifiers require a detailed segmentation of the vertebral endplate, and so we investigated the use of semi-automated segmentation methods as part of the diagnosis.Methods The vertebrae in a training set of 360 dual energy X-ray absorptiometry images were manually segmented. The shape and image texture of vertebrae were statistically modelled using Appearance Models. The vertebrae were given a gold standard classification by two radiologists. Linear discriminant classifiers to detect fractures were trained on the vertebral appearance model parameters. Classifier performance was evaluated by cross-validation for manual and semi-automatic segmentations, the latter derived using Active Appearance Models (AAM).Results were compared with a morphometric algorithm using the signs test. Results With manual segmentation, the false positive rates (FPR) at 95% sensitivity were: 5% (appearance) and 18% (morphometry). With semi-automatic segmentations the sensitivities at 5% FPR were: 88% (appearance) and 79% (morphometry).Conclusion Specificity and sensitivity are improved by using an appearance-based classifier compared to standard height ratio morphometry. An overall sensitivity loss of 7% occurs (at 95% specificity) when using a semi-automatic (AAM) segmentation compared to expert annotation, due to segmentation error. However, the classifier sensitivity is still adequate for a computer-assisted diagnosis system for vertebral fracture, especially if used in a triage approach.