Characterizing interactions between cardiac shape and deformation by non-linear manifold learning

• Published in: Medical image analysis Vol. 75; p. 102278
• Main Authors: Di Folco, Maxime, Moceri, Pamela, Clarysse, Patrick, Duchateau, Nicolas
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2022; Elsevier BV; Elsevier
• Subjects: Algorithms; Alignment; Cardiac function; Cardiac imaging; Computer applications; Computer Science; Datasets; Deformation; Dimensionality reduction; Disease; Echocardiography; Embedding; Heart; Heart - diagnostic imaging; Humans; Information fusion; Machine learning; Manifold learning; Manifolds (mathematics); Medical Imaging; Myocardial strain; Scalars; Ventricle; Dimensionality reduction; Manifold learning; Cardiac imaging; Myocardial strain; Information fusion; myocardial strain; dimensionality reduction; information fusion; manifold learning
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2021.102278

•Characterization of the interactions between myocardial shape and deformation.•We propose a manifold alignment strategy to handle partially related descriptors.•We demonstrate the benets of the joint analysis against the individual analysis of each descriptor.•Comparison of fusion and alignment strategies (1 latent space vs. 2 latent spaces). [Display omitted] In clinical routine, high-dimensional descriptors of the cardiac function such as shape and deformation are reduced to scalars (e.g. volumes or ejection fraction), which limit the characterization of complex diseases. Besides, these descriptors undergo interactions depending on disease, which may bias their computational analysis. In this paper, we aim at characterizing such interactions by unsupervised manifold learning. We propose to use a sparsified version of Multiple Manifold Learning to align the latent spaces encoding each descriptor and weighting the strength of the alignment depending on each pair of samples. While this framework was up to now only applied to link different datasets from the same manifold, we demonstrate its relevance to characterize the interactions between different but partially related descriptors of the cardiac function (shape and deformation). We benchmark our approach against linear and non-linear embedding strategies, among which the fusion of manifolds by Multiple Kernel Learning, the independent embedding of each descriptor by Diffusion Maps, and a strict alignment based on pairwise correspondences. We first evaluated the methods on a synthetic dataset from a 0D cardiac model where the interactions between descriptors are fully controlled. Then, we transfered them to a population of right ventricular meshes from 310 subjects (100 healthy and 210 patients with right ventricular disease) obtained from 3D echocardiography, where the link between shape and deformation is key for disease understanding. Our experiments underline the relevance of jointly considering shape and deformation descriptors, and that manifold alignment is preferable over fusion for our application. They also confirm at a finer scale the characteristic traits of the right ventricular diseases in our population.