期刊
FRONTIERS IN CARDIOVASCULAR MEDICINE
卷 7, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fcvm.2020.539788
关键词
cluster analysis; feature extraction; cine MRI; UK Biobank; cardiac pathology
资金
- European Research Council [MedYMA ERC-AdG-2011-291080]
- British Heart Foundation [PG/14/89/31194]
- AAP Sante [06 2017-260 DGA-DSH]
- Inria Sophia Antipolis - Mediterranee, NEF computation cluster
- EPSRC [EP/P001009/1] Funding Source: UKRI
We perform unsupervised analysis of image-derived shape and motion features extracted from 3,822 cardiac Magnetic resonance imaging (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 2 small clusters that probably correspond to 2 pathological categories. Further confirmation using a trained classification model and dimensionality reduction tools is carried out to support this finding. Moreover, we examine the differences between the other large clusters and compare our measures with the ground truth.
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