Deep Learning Surrogate of Computational Fluid Dynamics for Thrombus Formation Risk in the Left Atrial Appendage. Recently, the risk of thrombus formation in the left atrium (LA) has been assessed through patientspecific computational fluid dynamic (CFD) simulations, characterizing the complex 4D nature of blood flow in the left atrial appendage (LAA). Nevertheless, the vast computational resources and long computing times required by traditional CFD methods prevents its embedding in the clinical workflow of timesensitive applications. In this study, two distinct deep learning (DL) architectures have been developed to receive the patientspecific LAA geometry as an input and predict the endothelial cell activation potential (ECAP), which is linked to the risk of thrombosis. The first network is based on a simple fullyconnected network, while the latter also performs a dimensionality reduction of the variables. Both models have been trained with a synthetic dataset of 210 LAA geometries being able to accurately predict the ECAP distributions with an average error of 4.72% for the fullyconnected approach and 5.75% for its counterpart. Most importantly, the obtention of the ECAP predictions was quasiinstantaneous, orders of magnitude faster than conventional CFD.

Fully Automatic 3D BiAtria Segmentation from Late GadoliniumEnhanced MRIs Using Double Convolutional Neural Networks Segmentation of the 3D human atria from late gadoliniumenhanced (LGE)MRIs is crucial for understanding and analyzing the underlying atrial structures that sustain atrial fibrillation (AF), the most common cardiac arrhythmia. However, due to the lack of a large labeled dataset, current automated methods have only been developed for left atrium (LA) segmentation. Since AF is sustained across both the LA and right atrium (RA), an automatic biatria segmentation method is of high interest. We have therefore created a 3D LGEMRI database from AF patients with both LA and RA labels to train a double, sequentially used convolutional neural network (CNN) for automatic LA and RA epicardium and endocardium segmentation. To mitigate issues regarding the severe class imbalance and the complex geometry of the atria, the first CNN accurately detects the region of interest (ROI) containing the atria and the second CNN performs targeted regional segmentation of the ROI. The CNN comprises of a UNet backbone enhanced with residual blocks, preactivation normalization, and a Dice loss to improve accuracy and convergence. The receptive field of the CNN was increased by using 5 xc3x97 5 kernels to capture large variations in the atrial geometry. Our algorithm segments and reconstructs the LA and RA within 2 s, achieving a Dice accuracy of 94% and a surfacetosurface distance error of approximately 1 pixel. To our knowledge, the proposed approach is the first of its kind, and is currently the most robust automatic biatria segmentation method, creating a solid benchmark for future studies.