Multiview two-task recursive attention model for left atrium and atrial scars segmentation. Chen, J., Yang, G., Gao, Z., Ni, H., Angelini, E., Mohiaddin, R., Wong, T., Zhang, Y., Du, X., Zhang, H., Keegan, J., & Firmin, D. Volume 11071 LNCS , Springer International Publishing, 2018.
Multiview two-task recursive attention model for left atrium and atrial scars segmentation [pdf]Paper  Multiview two-task recursive attention model for left atrium and atrial scars segmentation [link]Website  doi  abstract   bibtex   
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.

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