Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank
This work addresses cardiac disease analysis for medical researchers, but it is incremental as it applies existing methods to a new dataset without introducing novel techniques.
The researchers tackled the problem of analyzing cardiac shape and motion from 4D MRIs in an unsupervised manner, using 3822 images from the UK Biobank, and identified two small clusters likely corresponding to pathological categories, with further validation through classification models and dimensionality reduction.
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.