@inproceedings{Chen:2019:10.1007/978-3-030-12029-0_32,
author = {Chen, C and Bai, W and Rueckert, D},
doi = {10.1007/978-3-030-12029-0_32},
pages = {292--301},
publisher = {Springer Verlag},
title = {Multi-task learning for left atrial segmentation on GE-MRI},
url = {http://dx.doi.org/10.1007/978-3-030-12029-0_32},
year = {2019}
}

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TY  - CPAPER
AB  - 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/.
AU  - Chen,C
AU  - Bai,W
AU  - Rueckert,D
DO  - 10.1007/978-3-030-12029-0_32
EP  - 301
PB  - Springer Verlag
PY  - 2019///
SN  - 0302-9743
SP  - 292
TI  - Multi-task learning for left atrial segmentation on GE-MRI
UR  - http://dx.doi.org/10.1007/978-3-030-12029-0_32
UR  - https://link.springer.com/chapter/10.1007%2F978-3-030-12029-0_32
UR  - http://hdl.handle.net/10044/1/72046
ER  - 

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