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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
Medical Research Council (MRC)
Medical Research Council (MRC)
Funder's Grant Number: EP/N014529/1
PO 11024
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