  Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in
atrial fibrillation (AF) patients has recently emerged as a promising technique
to stratify patients, guide ablation therapy and predict treatment success.
Visualisation and quantification of scar tissues require a segmentation of both
the left atrium (LA) and the high intensity scar regions from LGE-CMRI images.
These two segmentation tasks are challenging due to the cancelling of healthy
tissue signal, low signal-to-noise ratio and often limited image quality in
these patients. Most approaches require manual supervision and/or a second
bright-blood MRI acquisition for anatomical segmentation. Segmenting both the
LA anatomy and the scar tissues automatically from a single LGE-CMRI
acquisition is highly in demand. In this study, we proposed a novel fully
automated multiview two-task (MVTT) recursive attention model working directly
on LGE-CMRI images that combines a sequential learning and a dilated residual
learning to segment the LA (including attached pulmonary veins) and delineate
the atrial scars simultaneously via an innovative attention model. Compared to
other state-of-the-art methods, the proposed MVTT achieves compelling
improvement, enabling to generate a patient-specific anatomical and atrial scar
assessment model.
