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Magn Reson Imaging. 2019 Oct;62:70-77. doi: 10.1016/j.mri.2019.06.018. Epub 2019 Jun 24.

Anatomical context improves deep learning on the brain age estimation task.

Author information

1
Department of Biomedical Engineering, Featheringiill Hall 371, Vanderbilt University, 400 24(th) Ave S, Nashville, TN 37212, USA. Electronic address: camilo.bermudez@vanderbilt.edu.
2
Department of Computer Science, Featheringiill Hall 371, Vanderbilt University, 400 24(th) Ave S, Nashville, TN 37212, USA.
3
Department of Electrical Engineering, Featheringiill Hall 371, Vanderbilt University, 400 24(th) Ave S, Nashville, TN 37212, USA.
4
Department of Special Education, 230 Appleton Place, Vanderbilt University, Nashville, TN 37203, USA.
5
Laboratory of Behavioral Neuroscience, 251 Bayview Boulevard, National Institute on Aging, Baltimore, MD 21224, USA.
6
Department of Biomedical Engineering, Featheringiill Hall 371, Vanderbilt University, 400 24(th) Ave S, Nashville, TN 37212, USA; Department of Computer Science, Featheringiill Hall 371, Vanderbilt University, 400 24(th) Ave S, Nashville, TN 37212, USA; Department of Electrical Engineering, Featheringiill Hall 371, Vanderbilt University, 400 24(th) Ave S, Nashville, TN 37212, USA.

Abstract

Deep learning has shown remarkable improvements in the analysis of medical images without the need for engineered features. In this work, we hypothesize that deep learning is complementary to traditional feature estimation. We propose a network design to include traditional structural imaging features alongside deep convolutional ones and illustrate this approach on the task of imaging-based age prediction in two separate contexts: T1-weighted brain magnetic resonance imaging (MRI) (N = 5121, ages 4-96, healthy controls) and computed tomography (CT) of the head (N = 1313, ages 1-97, healthy controls). In brain MRI, we can predict age with a mean absolute error of 4.08 years by combining raw images along with engineered structural features, compared to 5.00 years using image-derived features alone and 8.23 years using structural features alone. In head CT, we can predict age with a median absolute error of 9.99 years combining features, compared to 11.02 years with image-derived features alone and 13.28 years with structural features alone. These results show that we can complement traditional feature estimation using deep learning to improve prediction tasks. As the field of medical image processing continues to integrate deep learning, it will be important to use the new techniques to complement traditional imaging features instead of fully displacing them.

KEYWORDS:

Brain age; Convolutional neural networks; Deep learning; Medical image processing

PMID:
31247249
PMCID:
PMC6689246
[Available on 2020-10-01]
DOI:
10.1016/j.mri.2019.06.018

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