CEREBRUM-7T: fast and fully volumetric brain segmentation of 7 Tesla MR volumes

Svanera, M. , Benini, S., Bontempi, D. and Muckli, L. (2021) CEREBRUM-7T: fast and fully volumetric brain segmentation of 7 Tesla MR volumes. Human Brain Mapping, 42(17), pp. 5563-5580. (doi: 10.1002/hbm.25636) (PMID:34598307)

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Abstract

Ultra-high-field magnetic resonance imaging (MRI) enables sub-millimetre resolution imaging of the human brain, allowing the study of functional circuits of cortical layers at the meso-scale. An essential step in many functional and structural neuroimaging studies is segmentation, the operation of partitioning the MR images in anatomical structures. Despite recent efforts in brain imaging analysis, the literature lacks in accurate and fast methods for segmenting 7-tesla (7T) brain MRI. We here present CEREBRUM-7T, an optimised end-to-end convolutional neural network, which allows fully automatic segmentation of a whole 7T T1w MRI brain volume at once, without partitioning the volume, pre-processing, nor aligning it to an atlas. The trained model is able to produce accurate multi-structure segmentation masks on six different classes plus background in only a few seconds. The experimental part, a combination of objective numerical evaluations and subjective analysis, confirms that the proposed solution outperforms the training labels it was trained on and is suitable for neuroimaging studies, such as layer functional MRI studies. Taking advantage of a fine-tuning operation on a reduced set of volumes, we also show how it is possible to effectively apply CEREBRUM-7T to different sites data. Furthermore, we release the code, 7T data, and other materials, including the training labels and the Turing test.

Item Type:Articles
Additional Information:For more information, please visit the project website at: https://rocknroll87q.github.io/cerebrum7t/
Keywords:Brain MRI segmentation, convolutional neural networks, weakly supervised learning, 3D image analysis.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Bontempi, Mr Dennis and Svanera, Dr Michele and Muckli, Professor Lars
Creator Roles:
Svanera, M.Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – review and editing, Visualization
Bontempi, D.Conceptualization, Methodology, Software, Writing – original draft, Writing – review and editing
Muckli, L.Resources, Funding acquisition
Authors: Svanera, M., Benini, S., Bontempi, D., and Muckli, L.
Subjects:Q Science > QM Human anatomy
College/School:College of Medical Veterinary and Life Sciences > Institute of Neuroscience and Psychology
Journal Name:Human Brain Mapping
Journal Abbr.:HBM
Publisher:Wiley
ISSN:1065-9471
ISSN (Online):1097-0193
Published Online:01 October 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Human Brain Mapping 42(17): 5563-5580
Publisher Policy:Reproduced under a Creative Commons License
Data DOI:10.25493/RF12-09N

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
307180Human Brain Project SGA_3Lars MuckliEuropean Commission (EC)945539NP - Centre for Cognitive Neuroimaging (CCNi)
785907Human Brain Project SGA_2Lars MuckliEuropean Commission (EC)UNSPECIFIEDNP - Centre for Cognitive Neuroimaging (CCNi)