Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning.

Rebsamen, Michael; Suter, Yannick; Wiest, Roland; Reyes, Mauricio; Rummel, Christian (2020). Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning. Frontiers in neurology, 11(244), p. 244. Frontiers Media S.A. 10.3389/fneur.2020.00244

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Motivation: Brain morphometry from magnetic resonance imaging (MRI) is a promising neuroimaging biomarker for the non-invasive diagnosis and monitoring of neurodegenerative and neurological disorders. Current tools for brain morphometry often come with a high computational burden, making them hard to use in clinical routine, where time is often an issue. We propose a deep learning-based approach to predict the volumes of anatomically delineated subcortical regions of interest (ROI), and mean thicknesses and curvatures of cortical parcellations directly from T1-weighted MRI. Advantages are the timely availability of results while maintaining a clinically relevant accuracy. Materials and Methods: An anonymized dataset of 574 subjects (443 healthy controls and 131 patients with epilepsy) was used for the supervised training of a convolutional neural network (CNN). A silver-standard ground truth was generated with FreeSurfer 6.0. Results: The CNN predicts a total of 165 morphometric measures directly from raw MR images. Analysis of the results using intraclass correlation coefficients showed, in general, good correlation with FreeSurfer generated ground truth data, with some of the regions nearly reaching human inter-rater performance (ICC > 0.75). Cortical thicknesses predicted by the CNN showed cross-sectional annual age-related gray matter atrophy rates both globally (thickness change of -0.004 mm/year) and regionally in agreement with the literature. A statistical test to dichotomize patients with epilepsy from healthy controls revealed similar effect sizes for structures affecting all subtypes as reported in a large-scale epilepsy study. Conclusions: We demonstrate the general feasibility of using deep learning to estimate human brain morphometry directly from T1-weighted MRI within seconds. A comparison of the results to other publications shows accuracies of comparable magnitudes for the subcortical volumes and cortical thicknesses.

Item Type:

Journal Article (Original Article)


10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Rebsamen, Michael Andreas, Suter, Yannick Raphael, Wiest, Roland Gerhard Rudi, Reyes Aguirre, Mauricio Antonio, Rummel, Christian


500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health




Frontiers Media S.A.




Martin Zbinden

Date Deposited:

12 May 2020 10:52

Last Modified:

02 Mar 2023 23:33

Publisher DOI:


PubMed ID:


Uncontrolled Keywords:

MRI cortical thickness deep learning epilepsy human brain morphometry




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