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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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Contributor : Qiao Zheng <>
Submitted on : Thursday, February 21, 2019 - 4:18:09 AM
Last modification on : Friday, October 11, 2019 - 2:25:35 PM
Document(s) archivé(s) le : Wednesday, May 22, 2019 - 1:00:35 PM


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  • HAL Id : hal-02043380, version 1
  • ARXIV : 1902.05811



Qiao Zheng, Hervé Delingette, Kenneth Fung, Steffen Petersen, Nicholas Ayache. Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank. 2019. ⟨hal-02043380⟩



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