Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternative for scar
assessment in patients with atrial fibrillation (AF). Automating the quantification and analysis of atrial scars can be
challenging due to the low image quality. In this work, we propose a fully automated method based on the graph-cuts
framework, where the potentials of the graph are learned on a surface mesh of the left atrium (LA) using a multi-scale
convolutional neural network (MS-CNN). For validation, we have included fifty-eight images with manual delineations.
MS-CNN, which can efficiently incorporate both the local and global texture information of the images, has been shown
to evidently improve the segmentation accuracy of the proposed graph-cuts based method. The segmentation could be
further improved when the contribution between the t-link and n-link weights of the graph is balanced. The proposed
method achieves a mean accuracy of 0.856 ± 0.033 and mean Dice score of 0.702 ± 0.071 for LA scar quantification.
Compared to the conventional methods, which are based on the manual delineation of LA for initialization, our method
is fully automatic and has demonstrated significantly better Dice score and accuracy (p < 0.01). The method is promising
and can be potentially useful in diagnosis and prognosis of AF.