Abstract
The combination of datasets is vital for providing increased statistical power, and is especially important for neurological conditions where limited data is available. However, our ability to combine datasets is limited by the addition of variance caused by factors such as differences in acquisition protocol and hardware. We aim to create scanner-invariant features using an iterative training scheme based on domain adaptation techniques, whilst simultaneously completing the desired segmentation task. We demonstrate the technique using an encoder-decoder architecture similar to the U-Net but expect that the proposed training scheme would be applicable to any feedforward network and task. We show that the network can be used to harmonise two datasets and also show that the network is applicable in the common scenario of limited available training data, meaning that the network should be applicable for real-world segmentation problems.
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Acknowledgements
ND is supported by the Engineering and Physical Sciences Research Council (EPSRC) and Medical Research Council (MRC) [grant number EP/L016052/1]. MJ is supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), and this research was funded by the Wellcome Trust [215573/Z/19/Z]. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust [203139/Z/16/Z]. AN is grateful for support from the UK Royal Academy of Engineering under the Engineering for Development Research Fellowships scheme.
The computational aspects of this research were supported by the Wellcome Trust Core Award [Grant Number 203141/Z/16/Z] and the NIHR Oxford BRC. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.
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Dinsdale, N.K., Jenkinson, M., Namburete, A.I.L. (2020). Unlearning Scanner Bias for MRI Harmonisation in Medical Image Segmentation. In: Papież, B., Namburete, A., Yaqub, M., Noble, J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, vol 1248. Springer, Cham. https://doi.org/10.1007/978-3-030-52791-4_2
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