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.
@article{Chen:2020:10.3389/fcvm.2020.00025,
author = {Chen, C and Qin, C and Qiu, H and Tarroni, G and Duan, J and Bai, W and Rueckert, D},
doi = {10.3389/fcvm.2020.00025},
journal = {Frontiers in Cardiovascular Medicine},
pages = {1--33},
title = {Deep learning for cardiac image segmentation: A review},
url = {http://dx.doi.org/10.3389/fcvm.2020.00025},
volume = {7},
year = {2020}
}
TY - JOUR
AB - Deep learning has become the most widely used approach for cardiac imagesegmentation in recent years. In this paper, we provide a review of over 100cardiac image segmentation papers using deep learning, which covers commonimaging modalities including magnetic resonance imaging (MRI), computedtomography (CT), and ultrasound (US) and major anatomical structures ofinterest (ventricles, atria and vessels). In addition, a summary of publiclyavailable cardiac image datasets and code repositories are included to providea base for encouraging reproducible research. Finally, we discuss thechallenges and limitations with current deep learning-based approaches(scarcity of labels, model generalizability across different domains,interpretability) and suggest potential directions for future research.
AU - Chen,C
AU - Qin,C
AU - Qiu,H
AU - Tarroni,G
AU - Duan,J
AU - Bai,W
AU - Rueckert,D
DO - 10.3389/fcvm.2020.00025
EP - 33
PY - 2020///
SN - 2297-055X
SP - 1
TI - Deep learning for cardiac image segmentation: A review
T2 - Frontiers in Cardiovascular Medicine
UR - http://dx.doi.org/10.3389/fcvm.2020.00025
UR - http://arxiv.org/abs/1911.03723v1
UR - https://www.frontiersin.org/articles/10.3389/fcvm.2020.00025/full
UR - http://hdl.handle.net/10044/1/77209
VL - 7
ER -