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

Abstract

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.

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“…from single nucleotide polymorphisms (SNP) genotype to predict the CVD risk. Other works detected CVD from cardiac magnetic resonance imaging (CMR) phenotypes and genetic data by means of ML and Mendelian randomization [26][27][28].…”

Characterization of surface motion patterns in highly deformable soft tissue organs from dynamic MRI: An application to assess 4D bladder motion

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