论文标题
白质高强度量和认知：对阿尔茨海默氏病神经成像倡议的深度学习病变检测和定量算法的评估
White matter hyperintensities volume and cognition: Assessment of a deep learning based lesion detection and quantification algorithm on the Alzheimers Disease Neuroimaging Initiative
论文作者
论文摘要
认知与白质超强度（WMH）体积之间的关系通常取决于使用的病变分割算法的准确性。因此，WMH的准确检测和量化是引起极大的兴趣。在这里，我们使用基于深度学习的WMH分割算法Stackgen-net来检测和量化ADNI的3D Flair量的WMH。我们使用了一部分受试者（n = 20），并通过经验丰富的神经放射科医生获得了手动WMH分割来证明我们算法的准确性。在较大的受试者队列（n = 290）上，我们观察到较大的WMH体积与执行功能的性能较差（P = .004），内存（P = .01）和语言（P = .005）相关。
The relationship between cognition and white matter hyperintensities (WMH) volumes often depends on the accuracy of the lesion segmentation algorithm used. As such, accurate detection and quantification of WMH is of great interest. Here, we use a deep learning-based WMH segmentation algorithm, StackGen-Net, to detect and quantify WMH on 3D FLAIR volumes from ADNI. We used a subset of subjects (n=20) and obtained manual WMH segmentations by an experienced neuro-radiologist to demonstrate the accuracy of our algorithm. On a larger cohort of subjects (n=290), we observed that larger WMH volumes correlated with worse performance on executive function (P=.004), memory (P=.01), and language (P=.005).
