  Late Gadolinium Enhancement Magnetic Resonance Imaging (LGE MRI) emerged as a
routine scan for patients with atrial fibrillation (AF). However, due to the
low image quality automating the quantification and analysis of the atrial
scars is challenging. In this study, we pro-posed a fully automated method
based on the graph-cuts framework, where the potential of the graph is learned
on a surface mesh of the left atrium (LA) using an equidistant projection and a
Deep Neural Network (DNN). For validation, we employed 100 datasets with manual
delineation. The results showed that the performance of the proposed method
improved and converged with respect to the increased size of training patches,
which provide important features of the structural and texture information
learned by the DNN. The segmentation could be further improved when the
contribution from the t-link and n-link is balanced, thanks to
inter-relationship learned by the DNN for the graph-cuts algorithm. Compared
with the published methods which mostly acquired manual delineation of the LA
or LA wall, our method is fully automatic and demonstrated evidently better
results with statistical significance. Finally, the accuracy of quantifying the
scars assessed by the Dice score was 0.570. The results are promising and the
method can be useful in diagnosis and prognosis of AF.
