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)

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 > School of Psychology & Neuroscience
• 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

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
307180		Human Brain Project SGA_3	Lars Muckli	European Commission (EC)	945539	NP - Centre for Cognitive Neuroimaging (CCNi)
785907		Human Brain Project SGA_2	Lars Muckli	European Commission (EC)	UNSPECIFIED	NP - Centre for Cognitive Neuroimaging (CCNi)