Atrial Scar Quantification via Multi-scale CNN in the Graph-cuts Framework
This work addresses the problem of automating scar assessment for atrial fibrillation diagnosis, offering a fully automatic solution that improves over manual-initialization methods, though it is incremental in the domain of medical image segmentation.
The paper tackled automated quantification of atrial scars from LGE MRI images in atrial fibrillation patients, achieving a mean accuracy of 0.856 and mean Dice score of 0.702 with a fully automatic method that outperforms conventional approaches.
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.