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.

Adversarial Convolutional Networks with Weak DomainTransfer for Multisequence Cardiac MR Images Segmentation Analysis and modeling of the ventricles and myocardium are important in the diagnostic and treatment of heart diseases. Manual delineation of those tissues in cardiac MR (CMR) scans is laborious and timeconsuming. The ambiguity of the boundaries makes the segmentation task rather challenging. Furthermore, the annotations on some modalities such as Late Gadolinium Enhancement (LGE) MRI, are often not available. We propose an endtoend segmentation framework based on convolutional neural network (CNN) and adversarial learning. A dilated residual Ushape network is used as a segmentor to generate the prediction mask; meanwhile, a CNN is utilized as a discriminator model to judge the segmentation quality. To leverage the available annotations across modalities per patient, a new loss function named weak domaintransfer loss is introduced to the pipeline. The proposed model is evaluated on the public dataset released by the challenge organizer in MICCAI 2019, which consists of 45 sets of multisequence CMR images. We demonstrate that the proposed adversarial pipeline outperforms baseline deeplearning methods.