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Semi-supervised learning for network-based cardiac MR image segmentation
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Title: | Semi-supervised learning for network-based cardiac MR image segmentation |
Authors: | Bai, W Oktay, O Sinclair, M Suzuki, H Rajchl, M Tarroni, G Glocker, B King, A Matthews, P Rueckert, D |
Item Type: | Conference Paper |
Abstract: | 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. |
Issue Date: | 4-Sep-2017 |
Date of Acceptance: | 16-May-2017 |
URI: | http://hdl.handle.net/10044/1/49165 |
DOI: | 10.1007/978-3-319-66185-8_29 |
ISSN: | 0302-9743 |
Publisher: | Springer Verlag |
Start Page: | 253 |
End Page: | 260 |
Journal / Book Title: | Lecture Notes in Computer Science |
Volume: | 1034 |
Copyright Statement: | © 2017 Springer International Publishing AG 2017. The final authenticated version is available online at https://doi.org/10.1007/978-3-319-66185-8_29 |
Sponsor/Funder: | Engineering & Physical Science Research Council (EPSRC) Commission of the European Communities Engineering & Physical Science Research Council (EPSRC) Biogen Idec Ltd UK DRI Ltd Medical Research Council (MRC) Medical Research Council (MRC) UK DRI Ltd |
Funder's Grant Number: | EP/N014529/1 655033 EP/P001009/1 PO 11024 4050641385 MR/M024903/1 4050641385 N/A |
Conference Name: | International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017 |
Keywords: | Artificial Intelligence & Image Processing |
Publication Status: | Published |
Start Date: | 2017-09-11 |
Finish Date: | 2017-09-13 |
Online Publication Date: | 2017-09-04 |
Appears in Collections: | Computing Department of Brain Sciences Faculty of Engineering |