We perform unsupervised analysis of image-derived shape and motion features extracted from 3822 cardiac 4D MRIs of the UK Biobank. First, with a feature extraction method previously published based on deep learning models, we extract from each case 9 feature values characterizing both the cardiac shape and motion. Second, a feature selection is performed to remove highly correlated feature pairs. Third, clustering is carried out using a Gaussian mixture model on the selected features. After analysis, we identify two small clusters which probably correspond to two pathological categories. Further confirmation using a trained classification model and dimensionality reduction tools is carried out to support this discovery. Moreover, we examine the differences between the other large clusters and compare our measures with the ground-truth.
翻译:我们对从英国生物库3822心脏4DMRIS中提取的图像生成的形状和运动特征进行了未经监督的分析。首先,我们采用了先前根据深层学习模型公布的特征提取方法,从每个案例中提取了心脏形状和运动特征的9个特征值。第二,进行了特征选择,以删除高度关联的特征配对。第三,在选定特征上使用高斯混合模型进行集群。经过分析,我们确定了两个小组群,它们可能与两种病理类别相对应。为了支持这一发现,我们使用了经过培训的分类模型和维度减少工具进一步确认。此外,我们考察了其他大组群群之间的差别,并将我们的措施与地面真相作了比较。