  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
employed 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 with 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 useful in diagnosis and prognosis of AF.
