In this section
@inproceedings{Bai:2017:10.1007/978-3-319-66185-8_29,
author = {Bai, W and Oktay, O and Sinclair, M and Suzuki, H and Rajchl, M and Tarroni, G and Glocker, B and King, A and Matthews, P and Rueckert, D},
doi = {10.1007/978-3-319-66185-8_29},
pages = {253--260},
publisher = {Springer Verlag},
title = {Semi-supervised learning for network-based cardiac MR image segmentation},
url = {http://dx.doi.org/10.1007/978-3-319-66185-8_29},
year = {2017}
}
TY - CPAPER
AB - Training a fully convolutional network for pixel-wise (or voxel-wise) image segmentation normally requires a large number of training images with corresponding ground truth label maps. However, it is a challenge to obtain such a large training set in the medical imaging domain, where expert annotations are time-consuming and difficult to obtain. In this paper, we propose a semi-supervised learning approach, in which a segmentation network is trained from both labelled and unlabelled data. The network parameters and the segmentations for the unlabelled data are alternately updated. We evaluate the method for short-axis cardiac MR image segmentation and it has demonstrated a high performance, outperforming a baseline supervised method. The mean Dice overlap metric is 0.92 for the left ventricular cavity, 0.85 for the myocardium and 0.89 for the right ventricular cavity. It also outperforms a state-of-the-art multi-atlas segmentation method by a large margin and the speed is substantially faster.
AU - Bai,W
AU - Oktay,O
AU - Sinclair,M
AU - Suzuki,H
AU - Rajchl,M
AU - Tarroni,G
AU - Glocker,B
AU - King,A
AU - Matthews,P
AU - Rueckert,D
DO - 10.1007/978-3-319-66185-8_29
EP - 260
PB - Springer Verlag
PY - 2017///
SN - 0302-9743
SP - 253
TI - Semi-supervised learning for network-based cardiac MR image segmentation
UR - http://dx.doi.org/10.1007/978-3-319-66185-8_29
UR - http://hdl.handle.net/10044/1/49165
ER -
