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| Item Type: | Preprint |
|---|---|
| Title: | Predicting brain-age from raw T(1)-weighted magnetic resonance imaging data using 3D convolutional neural networks |
| Creators Name: | Fisch, L., Ernsting, J., Winter, N.R., Holstein, V., Leenings, R., Beisemann, M., Sarink, K., Emden, D., Opel, N., Redlich, R., Repple, J., Grotegerd, D., Meinert, S., Wulms, N., Minnerup, H., Hirsch, J.G., Niendorf, T., Endemann, B., Bamberg, F., Kröncke, T., Peters, A., Bülow, R., Völzke, H., von Stackelberg, O., Sowade, R.F., Umutlu, L., Schmidt, B., Caspers, S., Kugel, H., Baune, B.T., Kircher, T., Risse, B., Dannlowski, U., Berger, K. and Hahn, T. |
| Abstract: | Age prediction based on Magnetic Resonance Imaging (MRI) data of the brain is a biomarker to quantify the progress of brain diseases and aging. Current approaches rely on preparing the data with multiple preprocessing steps, such as registering voxels to a standardized brain atlas, which yields a significant computational overhead, hampers widespread usage and results in the predicted brain-age to be sensitive to preprocessing parameters. Here we describe a 3D Convolutional Neural Network (CNN) based on the ResNet architecture being trained on raw, non-registered T(1)-weighted MRI data of N=10,691 samples from the German National Cohort and additionally applied and validated in N=2,173 samples from three independent studies using transfer learning. For comparison, state-of-the-art models using preprocessed neuroimaging data are trained and validated on the same samples. The 3D CNN using raw neuroimaging data predicts age with a mean average deviation of 2.84 years, outperforming the state-of-the-art brain-age models using preprocessed data. Since our approach is invariant to preprocessing software and parameter choices, it enables faster, more robust and more accurate brain-age modeling. |
| Keywords: | Machine Learning, Brain Age, Structural MRI, Raw MRI Scans, Neural Network |
| Source: | arXiv |
| Publisher: | Cornell University |
| Article Number: | 2103.11695 |
| Date: | 22 March 2021 |
| Official Publication: | https://arxiv.org/abs/2103.11695 |
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