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di Folco, M. ; Bernardino, G.* ; Clarysse, P.* ; Duchateau, N.*

Visualizing definitional divergence in high-dimensional data by manifold alignment: Application to 3D right ventricular strain computations.

IEEE Trans. Med. Imaging 45, 4327-4338 (2026)
Postprint DOI
Open Access Green
Medical imaging studies often rely on a single sample per subject, assuming it is representative of their physiological traits. However, variations in how input descriptors are defined or computed (e.g. due to a lack of consensus in the scientific field) may have a crucial impact on the analysis, and are hardly considered in practice. In this paper, we propose an original strategy based on representation learning to estimate a parametric map reflecting the impact of such definitional differences on a given physiological descriptor, previously extracted from medical images. We consider the different definitions or computations of such physiological descriptors as different high-dimensional data, potentially of heterogeneous types. We specifically focus on myocardial deformation (strain), for which there is limited agreement on its definition. We first use manifold alignment to match the latent representations associated with the different definitions of this descriptor. Then, we formulate plausible distributions in the latent space to represent definitional divergence across descriptors, from which we reconstruct a high-dimensional parametric map to visualize such definitional divergence. Due to the lack of proper ground truth for this specific clinical application, we first demonstrate this methodology on toy experiments and then expand the evaluation on right ventricular strain data from subjects obtained from 3D echocardiographic image sequences, for which different types of strain are available at each point of the right ventricle endocardial surface mesh. Beyond this illustrative application, our methodology has the potential to be generalised to many other population analyses considering heterogeneous high-dimensional descriptors.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter 3d Echocardiography ; Cardiac Imaging ; Information Fusion ; Myocardial Strain ; Representation Learning ; Uncertainty; Speckle-tracking Echocardiography; Uncertainty Quantification; Anatomy; Disease
ISSN (print) / ISBN 0278-0062
e-ISSN 1558-254X
Zeitschrift IEEE Transactions on Medical Imaging
Quellenangaben Band: 45, Heft: 8, Seiten: 4327-4338 Artikelnummer: , Supplement: ,
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Verlagsort New York, NY [u.a.]
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
Förderungen French National Research Agency (ANR) (LABEX PRIMES) of Univ Lyon
France 2030, Science et Technologie a Polytechnique Paris (STeP2)
Jeunes Chercheuses et Jeunes Chercheurs (JCJC) Project "Modeling hIerarchy between Cardiac descriptors with MAChine learning (MIC-MAC)"
Fondo social europeo (FSE+)
The "Agencia Estatal de investigacion" Ministerio de ciencia, Innovacion, y Universidades/Agencia estatal de Investigacion (MICIU/AEI)