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Proceedings Paper

Convolutional neural-network-based ordinal regression for brain age prediction from MRI scans
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Paper Abstract

Throughout human lifetime there are various ageing-related changes occurring. It has been demonstrated that departures from the healthy ageing trajectory can be used as a biomarker for several neurodegenerative conditions. Previous neuroimaging studies have used deep learning algorithms to investigate structural biomarkers including volumetric, microstructural and focal ones, using both cross-sectional and longitudinal cohorts. In this work, we would like to compare the metric and ordinal regression on the task of brain age prediction and devise a healthy whole brain ageing profile. For this purpose we have assembled a dataset comprising of 10,878 MRI T1-weighted scans acquired in healthy subjects. We have used the dataset to train a convolutional neural network-based ordinal regression model for age prediction using the imaging data with minimal pre-preprocessing (affine transformation and resampling onto a template). We leverage the distribution of predicted ages to propose ageing profile by assuming that the distribution of age predictions represents the distribution of age-related structural features. For comparison brain-ageing profile is obtained using the method of deep embedded clustering (DEC). Our ordinal regression model achieved a mean absolute error of 3.6 years. The results show that end-to-end learning of imaging features using an ordinal regression loss can yield age predictions that are comparable to, or even better, than metric-based regression models. Using DEC and the distribution of predictions resulted in an ageing profile consisting of 10 to 12 intervals. Resulting intervals in the ageing profile highlight the nonlinear nature of the ageing process in agreement with existing literature.

Paper Details

Date Published: 10 March 2020
PDF: 8 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113132B (10 March 2020); doi: 10.1117/12.2549636
Show Author Affiliations
Ksenia Sokolova, King's College London (United Kingdom)
Gareth J. Barker, King's College London (United Kingdom)
Giovanni Montana, The Univ. of Warwick (United Kingdom)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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