Srikrishna, M;
Pereira, JB;
Heckemann, RA;
Volpe, G;
van Westen, D;
Zettergren, A;
Kern, S;
... Schöll, M; + view all
(2021)
Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT.
Neuroimage
, 244
, Article 118606. 10.1016/j.neuroimage.2021.118606.
Preview |
Text
Scholl_Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT_VoR.pdf - Published Version Download (3MB) | Preview |
Abstract
Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more widely available than MR scans, their application is currently limited to the visual assessment of brain integrity and the exclusion of co-pathologies. CT has rarely been used for tissue classification because the contrast between grey matter and white matter was considered insufficient. In this study, we propose an automatic method for segmenting grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and intracranial volume (ICV) from head CT images. A U-Net deep learning model was trained and validated on CT images with MRI-derived segmentation labels. We used data from 744 participants of the Gothenburg H70 Birth Cohort Studies for whom CT and T1-weighted MR images had been acquired on the same day. Our proposed model predicted brain tissue classes accurately from unseen CT images (Dice coefficients of 0.79, 0.82, 0.75, 0.93 and 0.98 for GM, WM, CSF, brain volume and ICV, respectively). To contextualize these results, we generated benchmarks based on established MR-based methods and intentional image degradation. Our findings demonstrate that CT-derived segmentations can be used to delineate and quantify brain tissues, opening new possibilities for the use of CT in clinical practice and research.
| Type: | Article |
|---|---|
| Title: | Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.neuroimage.2021.118606 |
| Publisher version: | https://doi.org/10.1016/j.neuroimage.2021.118606 |
| Language: | English |
| Additional information: | © 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ) |
| Keywords: | Brain image segmentation, Convolutional neural networks (CNN), Deep learning, computed tomography (CT) |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10137195 |
Archive Staff Only
 |
View Item |
