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

Ksenia Sokolova; Gareth J. Barker; Giovanni Montana

doi:10.1117/12.2549636

10 March 2020 Convolutional neural-network-based ordinal regression for brain age prediction from MRI scans

Ksenia Sokolova, Gareth J. Barker, Giovanni Montana

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113132B (2020) https://doi.org/10.1117/12.2549636
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Citation Download Citation

Ksenia Sokolova, Gareth J. Barker, and Giovanni Montana "Convolutional neural-network-based ordinal regression for brain age prediction from MRI scans", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113132B (10 March 2020); https://doi.org/10.1117/12.2549636

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