Publications from our Researchers

Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.  

Citation

BibTex format

@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}
}

RIS format (EndNote, RefMan)

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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