Semi-supervised learning for network-based cardiac MR image segmentation

Bai W, Oktay O, Sinclair M, Suzuki H, Rajchl M, Tarroni G, Glocker B, King A, Matthews PM, Rueckert D (2017)

Publication Type: Conference contribution

Publication year: 2017

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 10434 LNCS

Pages Range: 253-260

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Quebec City, QC, CAN

ISBN: 9783319661841

DOI: 10.1007/978-3-319-66185-8_29

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.

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) King’s College London GB United Kingdom (GB)

How to cite

APA:

Bai, W., Oktay, O., Sinclair, M., Suzuki, H., Rajchl, M., Tarroni, G.,... Rueckert, D. (2017). Semi-supervised learning for network-based cardiac MR image segmentation. In Pierre Jannin, Simon Duchesne, Maxime Descoteaux, Alfred Franz, D. Louis Collins, Lena Maier-Hein (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 253-260). Quebec City, QC, CAN: Springer Verlag.

MLA:

Bai, Wenjia, et al. "Semi-supervised learning for network-based cardiac MR image segmentation." Proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, Quebec City, QC, CAN Ed. Pierre Jannin, Simon Duchesne, Maxime Descoteaux, Alfred Franz, D. Louis Collins, Lena Maier-Hein, Springer Verlag, 2017. 253-260.

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