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首页> 美国卫生研究院文献>Frontiers in Neuroinformatics >Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm
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Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm

机译：基于监督机器学习算法的结构磁共振脑图像自动质量评估

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High-resolution three-dimensional magnetic resonance imaging (3D-MRI) is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM) algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy) of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.
机译：高分辨率三维磁共振成像（3D-MRI）越来越多地用于描述神经精神疾病的潜在形态变化。不幸的是，伪影经常损害3D-MRI的效用，从I型和II型错误中产生无法再现的结果。因此，在使用前对3D-MRI进行伪影筛查至关重要。当前，质量评估涉及对3D-MRI体积的逐层视觉检查，该过程既主观又耗时。使3D-MRI的质量评级自动化可以提高该过程的效率和可重复性。本研究是使用内部开发的全局和目标区域（ROI）自动化图像质量功能，在结构性脑部图像质量评估中应用支持向量机（SVM）算法的首批工作之一。 SVM是一种受监督的机器学习算法，可以基于从学习数据集中获取的知识来预测测试数据集的类别。通过将SVM预测的质量标签与研究者确定的质量标签进行比较，评估了自动化SVM方法的性能（准确性）。使用SVM方法对我们数据库中的1457 3D-MRI体积进行分类的准确性约为80％。这些结果令人鼓舞，并说明了将SVM用作3D-MRI自动化质量评估工具的可能性。

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