  Segmentation of the left atrium (LA) is crucial for assessing its anatomy in
both pre-operative atrial fibrillation (AF) ablation planning and
post-operative follow-up studies. In this paper, we present a fully automated
framework for left atrial segmentation in gadolinium-enhanced magnetic
resonance images (GE-MRI) based on deep learning. We propose a fully
convolutional neural network and explore the benefits of multi-task learning
for performing both atrial segmentation and pre/post ablation classification.
Our results show that, by sharing features between related tasks, the network
can gain additional anatomical information and achieve more accurate atrial
segmentation, leading to a mean Dice score of 0.901 on a test set of 20 3D MRI
images. Code of our proposed algorithm is available at
https://github.com/cherise215/atria_segmentation_2018/.

Computer Vision and Pattern Recognition;Machine Learning