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We perform unsupervised analysis of image-derived shape and motion featuresextracted from 3822 cardiac 4D MRIs of the UK Biobank. First, with a featureextraction method previously published based on deep learning models, weextract from each case 9 feature values characterizing both the cardiac shapeand motion. Second, a feature selection is performed to remove highlycorrelated feature pairs. Third, clustering is carried out using a Gaussianmixture model on the selected features. After analysis, we identify two smallclusters which probably correspond to two pathological categories. Furtherconfirmation using a trained classification model and dimensionality reductiontools is carried out to support this discovery. Moreover, we examine thedifferences between the other large clusters and compare our measures with theground-truth.

Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank