DL+DiReCT – Direct Cortical Thickness Estimation using Deep Learning-based Anatomy Segmentation and Cortex Parcellation


DL+DiReCT combines a deep learning-based neuroanatomy segmentation and cortex parcellation with a diffeomorphic registration technique to measure cortical thickness from T1-weighted magnetic resonance images.


Michael Rebsamen1,2

Christian Rumme1

Mauricio Reyes3,4

Roland Wiest1

Richard McKinley1



Estimated cost

Total price: Free


Stable v1

License: BSD 3

DL+DiReCT is a method using high-quality deep learning (DL) based neuroanatomy segmentations followed by diffeomorphic registration-based cortical thickness (DiReCT), yielding accurate and reliable cortical thickness measures in a short time. DiReCT is a known technique to derive such measures from non-surface-based volumetric tissue maps. ANTs provides an open-source method for estimating cortical thickness, derived by applying DiReCT to an atlas-based segmentation.

Accurate and reliable measures of cortical thickness from magnetic resonance imaging are an important biomarker to study neurodegenerative and neurological disorders.

Rebsamen, Michael, et al. “Direct cortical thickness estimation using deep learning‐based anatomy segmentation and cortex parcellation.” Human brain mapping 41.17 (2020): 4804-4814.



1Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland
2Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
3Insel Data Science Center, Inselspital, Bern University Hospital, Bern, Switzerland
4ARTORG Center for Biomedical Research, University of Bern, Bern, Switzerland