Learning Interactions Between Cardiac Shape and Deformation: Application to Pulmonary Hypertension Cardiac shape and deformation are two relevant descriptors for the characterization of cardiovascular diseases. It is also known that strong interactions exist between them depending on the disease. In clinical routine, these high dimensional descriptors are reduced to scalar values (ventricular ejection fraction, volumes, global strains...), leading to a substantial loss of information. Methods exist to better integrate these highdimensional data by reducing the dimension and mixing heterogeneous descriptors. Nevertheless, they usually do not consider the interactions between the descriptors. In this paper, we propose to apply dimensionality reduction on high dimensional cardiac shape and deformation descriptors and take into account their interactions. We investigated two unsupervised linear approaches, an individual analysis of each feature (Principal Component Analysis), and a joint analysis of both features (Partial Least Squares) and related their output to the main characteristics of the studied pathology. We experimented both methods on right ventricular meshes from a population of 254 cases tracked along the cycle (154 with pulmonary hypertension, 100 controls). Despite similarities in the output space obtained by the two methods, substantial differences are observed in the reconstructed shape and deformation patterns along the principal modes of variation, in particular in regions of interest for the studied disease.

Conditional Generative Adversarial Networks for the Prediction of Cardiac Contraction from Individual Frames Cardiac anatomy and function are interrelated in many ways, and these relations can be affected by multiple pathologies. In particular, this applies to ventricular shape and mechanical deformation. We propose a machine learning approach to capture these interactions by using a conditional Generative Adversarial Network (cGAN) to predict cardiac deformation from individual Cardiac Magnetic Resonance (CMR) frames, learning a deterministic mapping between enddiastolic (ED) to endsystolic (ES) CMR shortaxis frames. We validate the predicted images by quantifying the difference with real images using mean squared error (MSE) and structural similarity index (SSIM), as well as the Dice coefficient between their respective endo and epicardial segmentations, obtained with an additional UNet. We evaluate the ability of the network to learn xe2x80x9chealthyxe2x80x9d deformations by training it on (sim )33,500 image pairs from (sim )12,000 subjects, and testing on a separate test set of (sim )4,500 image pairs from the UK Biobank study. Mean MSE, SSIM and Dice scores were 0.0026 xc2xb1 0.0013, 0.89 xc2xb1 0.032 and 0.89 xc2xb1 0.059 respectively. We subsequently retrained the network on specific patient group data, showing that the network is capable of extracting physiologically meaningful differences between patient populations suggesting promising applications on pathological data.