Unlearning Scanner Bias for MRI Harmonisation in Medical Image Segmentation

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1248)


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.


Harmonisation Joint domain adaptation MRI 



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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Wellcome Centre for Integrative Neuroimaging, FMRIBUniversity of OxfordOxfordUK
  2. 2.Australian Institute for Machine Learning (AIML), Department of Computer ScienceUniversity of AdelaideAdelaideAustralia
  3. 3.South Australian Health and Medical Research Institute (SAHMRI)AdelaideAustralia
  4. 4.Institute of Biomedical EngineeringUniversity of OxfordOxfordUK

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