Highlights We demonstrate a flexible deep-learning-based harmonisation framework. Applied to age prediction and segmentation tasks in a range of datasets. Scanner information is removed, maintaining performance and improving generalisability. The framework can be used with any feedforward network architecture. It successfully removes additional confounds and works with varied distributions.

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Increasingly large MRI neuroimaging datasets are becoming available, including many highly multi-site multi-scanner datasets. Combining the data from the different scanners is vital for increased statistical power; however, this leads to an increase in variance due to nonbiological factors such as the differences in acquisition protocols and hardware, which can mask signals of interest.We propose a deep learning based training scheme, inspired by domain adaptation techniques, which uses an iterative update approach to aim to create scanner-invariant features while simultaneously maintaining performance on the main task of interest, thus reducing the influence of scanner on network predictions. We demonstrate the framework for regression, classification and segmentation tasks and two different network architectures.We show that not only can the framework harmonise many-site datasets but it can also adapt to many data scenarios, including biased datasets and limited training labels. Finally, we show that the framework can be extended for the removal of other known confounds in addition to scanner. The overall framework is therefore flexible and should be applicable to a wide range of neuroimaging studies.

Accurate segmentations of neuroanatomical structures are essential for volumetric and morphological assessment but manual segmentation is time-consuming and error-prone. We propose a convolutional neural network for structural segmentation based on deformation of a an example mask that is disease-state agnostic, which we apply to the hippocampus. The hippocampus is one of the first subcortical structures affected by Alzheimer's disease, atrophying as the disease progresses. As the disease state is not always known, and due to the varying degrees of atrophy, an accurate shape prior is not always available. The network is based on an adapted spatial transformer network that learns a deformation field to resample an initial binary mask, to create an output segmentation. This segmentation is learnt by the network from the input T1-weighted MRI in an end-to-end manner. Experiments on the HarP dataset show that the network outperforms other segmentation methods and is consistent across disease states, independent of the degree of disease-related atrophy. We also explore the effect of the initial binary mask on the segmentation and show that the segmentation is insensitive to the initialisation of this mask.

Both normal ageing and neurodegenerative diseases cause morphological changes to the brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally heterogenous, both within a subject and across a population. Machine learning models are particularly suited to capture these patterns and can produce a model that is sensitive to changes of interest, despite the large variety in healthy brain appearance. In this paper, the power of convolutional neural networks (CNNs) and the rich UK Biobank dataset, the largest database currently available, are harnessed to address the problem of predicting brain age. We developed a 3D CNN architecture to predict chronological age, using a training dataset of 12,802 T1-weighted MRI images and a further 6,885 images for testing. The proposed method shows competitive performance on age prediction, but, most importantly, the CNN prediction errors ΔBrain Age = AgePredicted - AgeTrue correlated significantly with many clinical measurements from the UK Biobank in the female and male groups. In addition, having used images from only one imaging modality in this experiment, we examined the relationship between ΔBrain Age and the image-derived phenotypes (IDPs) from all other imaging modalities in the UK Biobank, showing correlations consistent with known patterns of ageing. Furthermore, we show that the use of nonlinearly registered images to train CNNs can lead to the network being driven by artefacts of the registration process and missing subtle indicators of ageing, limiting the clinical relevance. Due to the longitudinal aspect of the UK Biobank study, in the future it will be possible to explore whether the ΔBrain Age from models such as this network were predictive of any health outcomes.

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Combining datasets is vital for increased statistical power, especially for neurological conditions where limited data is available. However, variance due to differences in acquisition protocol and hardware limits our ability to combine datasets. We propose an iterative training scheme based on domain adaptation techniques, aiming to create scanner-invariant features while simultaneously maintaining overall performance on the main task. We demonstrate this on age prediction, but expect that our proposed training scheme will be applicable to any feedforward network and classification or regression task. We show that not only can we harmonise three MRI datasets from different studies, but can also successfully adapt the training to work with very biased datasets. The training scheme should, therefore, be applicable to most real-world data scenarios, enabling harmonisation for the task of interest.

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