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:2019:10.1007/978-3-030-32245-8_58,
author = {Chen, C and Biffi, C and Tarroni, G and Petersen, S and Bai, W and Rueckert, D},
doi = {10.1007/978-3-030-32245-8_58},
pages = {523--531},
publisher = {Springer International Publishing},
title = {Learning shape priors for robust cardiac MR segmentation from multi-view images},
url = {http://dx.doi.org/10.1007/978-3-030-32245-8_58},
year = {2019}
}
TY - CPAPER
AB - Cardiac MR image segmentation is essential for the morphological and functional analysis of the heart. Inspired by how experienced clinicians assess the cardiac morphology and function across multiple standard views (i.e. long- and short-axis views), we propose a novel approach which learns anatomical shape priors across different 2D standard views and leverages these priors to segment the left ventricular (LV) myocardium from short-axis MR image stacks. The proposed segmentation method has the advantage of being a 2D network but at the same time incorporates spatial context from multiple, complementary views that span a 3D space. Our method achieves accurate and robust segmentation of the myocardium across different short-axis slices (from apex to base), outperforming baseline models (e.g. 2D U-Net, 3D U-Net) while achieving higher data efficiency. Compared to the 2D U-Net, the proposed method reduces the mean Hausdorff distance (mm) from 3.24 to 2.49 on the apical slices, from 2.34 to 2.09 on the middle slices and from 3.62 to 2.76 on the basal slices on the test set, when only 10% of the training data was used.
AU - Chen,C
AU - Biffi,C
AU - Tarroni,G
AU - Petersen,S
AU - Bai,W
AU - Rueckert,D
DO - 10.1007/978-3-030-32245-8_58
EP - 531
PB - Springer International Publishing
PY - 2019///
SN - 0302-9743
SP - 523
TI - Learning shape priors for robust cardiac MR segmentation from multi-view images
UR - http://dx.doi.org/10.1007/978-3-030-32245-8_58
UR - https://link.springer.com/chapter/10.1007%2F978-3-030-32245-8_58
UR - http://hdl.handle.net/10044/1/77443
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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