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Unsupervised domain adaptation in brain lesion segmentation with adversarial networks

Kamnitsas K, Baumgartner C, Ledig C, Newcombe V, Simpson J, Kane A, Menon D, Nori A, Criminisi A, Rueckert D, Glocker B (2017)

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Publication Type: Conference contribution

Publication year: 2017

Journal

Publisher: Springer Verlag

Book Volume: 10265 LNCS

Pages Range: 597-609

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

Event location: Boone, NC, USA

ISBN: 9783319590493

DOI: 10.1007/978-3-319-59050-9_47

Abstract

Significant advances have been made towards building accurate automatic segmentation systems for a variety of biomedical applications using machine learning. However, the performance of these systems often degrades when they are applied on new data that differ from the training data, for example, due to variations in imaging protocols. Manually annotating new data for each test domain is not a feasible solution. In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more robust to differences in the input data, and which does not require any annotations on the test domain. Specifically, we derive domain-invariant features by learning to counter an adversarial network, which attempts to classify the domain of the input data by observing the activations of the segmentation network. Furthermore, we propose a multi-connected domain discriminator for improved adversarial training. Our system is evaluated using two MR databases of subjects with traumatic brain injuries, acquired using different scanners and imaging protocols. Using our unsupervised approach, we obtain segmentation accuracies which are close to the upper bound of supervised domain adaptation.

Involved external institutions

How to cite

APA:

Kamnitsas, K., Baumgartner, C., Ledig, C., Newcombe, V., Simpson, J., Kane, A.,... Glocker, B. (2017). Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In Hongtu Zhu, Marc Niethammer, Martin Styner, Hongtu Zhu, Dinggang Shen, Pew-Thian Yap, Stephen Aylward, Ipek Oguz (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 597-609). Boone, NC, USA: Springer Verlag.

MLA:

Kamnitsas, Konstantinos, et al. "Unsupervised domain adaptation in brain lesion segmentation with adversarial networks." Proceedings of the 25th International Conference on Information Processing in Medical Imaging, IPMI 2017, Boone, NC, USA Ed. Hongtu Zhu, Marc Niethammer, Martin Styner, Hongtu Zhu, Dinggang Shen, Pew-Thian Yap, Stephen Aylward, Ipek Oguz, Springer Verlag, 2017. 597-609.

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