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.
K. Kamnitsas—Part of this work was carried on when KK was an intern at Microsoft Research.
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Acknowledgements
This work is supported by the EPSRC (grant No: EP/N023668/1) and partially funded by an European Union Framework Program 7 grant (CENTER-TBI; Agreement No: 60215). Part of this work was carried on when KK was an intern at Microsoft Research Cambridge. KK is also supported by the President’s Ph.D. Scholarship of Imperial College London. VN is supported by an Academy of Medical Sciences/Health Foundation Clinician Scientist Fellowship. DM is supported by the Neuroscience Theme of the NIHR Cambridge Biomedical Research Centre and NIHR Senior Investigator awards. We gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs.
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Kamnitsas, K. et al. (2017). Unsupervised Domain Adaptation in Brain Lesion Segmentation with Adversarial Networks. In: Niethammer, M., et al. Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer Science(), vol 10265. Springer, Cham. https://doi.org/10.1007/978-3-319-59050-9_47
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