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.

Combining MultiSequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI Accurate segmentation of the cardiac boundaries in late gadolinium enhancement magnetic resonance images (LGEMRI) is a fundamental step for accurate quantification of scar tissue. However, while there are many solutions for automatic cardiac segmentation of cine images, the presence of scar tissue can make the correct delineation of the myocardium in LGEMRI challenging even for human experts. As part of the MultiSequence Cardiac MR Segmentation Challenge, we propose a solution for LGEMRI segmentation based on two components. First, a generative adversarial network is trained for the task of modalitytomodality translation between cine and LGEMRI sequences to obtain extra synthetic images for both modalities. Second, a deep learning model is trained for segmentation with different combinations of original, augmented and synthetic sequences. Our results based on three magnetic resonance sequences (LGE, bSSFP and T2) from 45 different patients show that the multisequence model training integrating synthetic images and data augmentation improves in the segmentation over conventional training with real datasets. In conclusion, the accuracy of the segmentation of LGEMRI images can be improved by using complementary information provided by noncontrast MRI sequences.