In this section
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.
@inproceedings{Chen:2020:10.1007/978-3-030-39074-7_22,
author = {Chen, C and Ouyang, C and Tarroni, G and Schlemper, J and Qiu, H and Bai, W and Rueckert, D},
doi = {10.1007/978-3-030-39074-7_22},
pages = {209--219},
publisher = {Springer International Publishing},
title = {Unsupervised multi-modal style transfer for cardiac MR segmentation},
url = {http://dx.doi.org/10.1007/978-3-030-39074-7_22},
year = {2020}
}
TY - CPAPER
AB - In this work, we present a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead by transferring the anatomical knowledge and features learned on annotated balanced steady-state free precession (bSSFP) images, which are easier to acquire. Our framework mainly consists of two neural networks: a multi-modal image translation network for style transfer and a cascaded segmentation network for image segmentation. The multi-modal image translation network generates realistic and diverse synthetic LGE images conditioned on a single annotated bSSFP image, forming a synthetic LGE training set. This set is then utilized to fine-tune the segmentation network pre-trained on labelled bSSFP images, achieving the goal of unsupervised LGE image segmentation. In particular, the proposed cascaded segmentation network is able to produce accurate segmentation by taking both shape prior and image appearance into account, achieving an average Dice score of 0.92 for the left ventricle, 0.83 for the myocardium, and 0.88 for the right ventricle on the test set.
AU - Chen,C
AU - Ouyang,C
AU - Tarroni,G
AU - Schlemper,J
AU - Qiu,H
AU - Bai,W
AU - Rueckert,D
DO - 10.1007/978-3-030-39074-7_22
EP - 219
PB - Springer International Publishing
PY - 2020///
SN - 0302-9743
SP - 209
TI - Unsupervised multi-modal style transfer for cardiac MR segmentation
UR - http://dx.doi.org/10.1007/978-3-030-39074-7_22
UR - https://link.springer.com/chapter/10.1007%2F978-3-030-39074-7_22
UR - http://hdl.handle.net/10044/1/77444
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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