Accurate brain age prediction using recurrent slice-based networks

Pradeep K. Lam; Vigneshwaran Santhalingam; Parth Suresh; Rahul Baboota; Alyssa H. Zhu; Sophia I. Thomopoulos; Neda Jahanshad; Paul M. Thompson

doi:10.1117/12.2579630

3 November 2020 Accurate brain age prediction using recurrent slice-based networks

Pradeep K. Lam, Vigneshwaran Santhalingam, Parth Suresh, Rahul Baboota, Alyssa H. Zhu, Sophia I. Thomopoulos, Neda Jahanshad, Paul M. Thompson

Author Affiliations +

Proceedings Volume 11583, 16th International Symposium on Medical Information Processing and Analysis; 1158303 (2020) https://doi.org/10.1117/12.2579630
Event: The 16th International Symposium on Medical Information Processing and Analysis, 2020, Lima, Peru

Abstract

BrainAge (a subject’s apparent age predicted from neuroimaging data) is an important biomarker of brain aging. The deviation of BrainAge from true age has been associated with psychiatric and neurological disease, and has proven effective in predicting conversion from mild cognitive impairment (MCI) to dementia. Conventionally, 3D convolutional neural networks and their variants are used for brain age prediction. However, these networks have a larger number of parameters and take longer to train than their 2D counterparts. Here we propose a 2D slice-based recurrent neural network model, which takes in an ordered sequence of sagittal slices as input to predict the brain age. The model consists of two components: a 2D convolutional neural network (CNN), which encodes the relevant features from the slices, and a recurrent neural network (RNN) that learns the relationship between slices. We compare our method to other recently proposed methods, including 3D deep convolutional regression networks, information theoretic models, and bag-of-features (BoF) models (such as BagNet) - where the classification is based on the occurrences of local features, without taking into consideration their global spatial ordering. In our experiments, our proposed model performs comparably to, or better than, the current state of the art models, with nearly half the number of parameters and a lower convergence time.

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Pradeep K. Lam, Vigneshwaran Santhalingam, Parth Suresh, Rahul Baboota, Alyssa H. Zhu, Sophia I. Thomopoulos, Neda Jahanshad, and Paul M. Thompson "Accurate brain age prediction using recurrent slice-based networks", Proc. SPIE 11583, 16th International Symposium on Medical Information Processing and Analysis, 1158303 (3 November 2020); https://doi.org/10.1117/12.2579630

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