Classification of amyloid status using machine learning with histograms of oriented 3D gradients.
Classification of amyloid status using machine learning with histograms of oriented 3D gradients.
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DOI：
10.1016/j.nicl.2016.05.004
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Alzheimer's Disease Neuroimaging Initiative
中科院分区：
文献类型：
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作者：
Cattell L;Platsch G;Pfeiffer R;Declerck J;Schnabel JA;Hutton C;Alzheimer's Disease Neuroimaging Initiative
脑淀粉样蛋白负荷可通过正电子发射断层扫描成像使用标准化摄取值比率进行定量评估。使用这些比率作为视觉图像评估的辅助手段已被证明可提高阅片者间的可靠性，然而，淀粉样蛋白阳性阈值取决于用于计算摄取比率的示踪剂和特定图像区域。为了解决这个问题，我们提出了一种机器学习方法来进行淀粉样蛋白状态分类，该方法独立于示踪剂，并且不需要特定的感兴趣区域集。我们的方法提取特征向量的淀粉样蛋白的图像，这是基于直方图的定向三维梯度。我们在133个18F-florbetapir脑体积上优化了我们的方法，并将其应用于131个体积的单独测试集。使用相同的参数设置，然后我们将我们的方法应用于209个11 C-PiB图像和128个18F-florbetaben图像。我们将我们的方法与使用其他两种方法获得的分类结果进行了比较：标准化摄取值比率和基于体素强度的机器学习方法。我们的方法导致最大的平均距离之间的主题和分类边界，这表明它是不太可能作出低置信度的分类决策。此外，我们的方法获得了最高的分类精度为所有三个示踪剂，并始终达到96%以上的准确率。提出了一种用于脑淀粉样蛋白状态分类的机器学习方法。该方法不依赖于PET示踪剂，几乎不需要重新校准。三种淀粉样蛋白示踪剂的分类准确率高于SUVR。
Brain amyloid burden may be quantitatively assessed from positron emission tomography imaging using standardised uptake value ratios. Using these ratios as an adjunct to visual image assessment has been shown to improve inter-reader reliability, however, the amyloid positivity threshold is dependent on the tracer and specific image regions used to calculate the uptake ratio. To address this problem, we propose a machine learning approach to amyloid status classification, which is independent of tracer and does not require a specific set of regions of interest. Our method extracts feature vectors from amyloid images, which are based on histograms of oriented three-dimensional gradients. We optimised our method on 133 18F-florbetapir brain volumes, and applied it to a separate test set of 131 volumes. Using the same parameter settings, we then applied our method to 209 11C-PiB images and 128 18F-florbetaben images. We compared our method to classification results achieved using two other methods: standardised uptake value ratios and a machine learning method based on voxel intensities. Our method resulted in the largest mean distances between the subjects and the classification boundary, suggesting that it is less likely to make low-confidence classification decisions. Moreover, our method obtained the highest classification accuracy for all three tracers, and consistently achieved above 96% accuracy. A machine learning approach to brain amyloid status classification is proposed. The method is independent of PET tracer and requires little recalibration. Classification accuracy was higher than SUVR for three amyloid tracers.