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Yu, Z.* ; Han, X.* ; Xu, W.* ; Zhang, J.* ; Marr, C. ; Shen, D.* ; Peng, T. ; Zhang, X.Y.* ; Feng, J.*

A generalizable brain extraction net (BEN) for multimodal MRI data from rodents, nonhuman primates, and humans.

eLife 11:e81217 (2022)

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	Open Access Gold

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Accurate brain tissue extraction on magnetic resonance imaging (MRI) data is crucial for analyzing brain structure and function. While several conventional tools have been optimized to handle human brain data, there have been no generalizable methods to extract brain tissues for multimodal MRI data from rodents, nonhuman primates, and humans. Therefore, developing a flexible and generalizable method for extracting whole brain tissue across species would allow researchers to analyze and compare experiment results more efficiently. Here, we propose a domain-adaptive and semi-supervised deep neural network, named the Brain Extraction Net (BEN), to extract brain tissues across species, MRI modalities, and MR scanners. We have evaluated BEN on 18 independent datasets, including 783 rodent MRI scans, 246 nonhuman primate MRI scans, and 4,601 human MRI scans, covering five species, four modalities, and six MR scanners with various magnetic field strengths. Compared to conventional toolboxes, the superiority of BEN is illustrated by its robustness, accuracy, and generalizability. Our proposed method not only provides a generalized solution for extracting brain tissue across species but also significantly improves the accuracy of atlas registration, thereby benefiting the downstream processing tasks. As a novel fully automated deep-learning method, BEN is designed as an open-source software to enable high-throughput processing of neuroimaging data across species in preclinical and clinical applications.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Computational Biology ; Human ; Mouse ; Neuroscience ; Rat ; Systems Biology; Uncertainty Estimation; Rat-brain; Image; Segmentation; Atlas

ISSN (print) / ISBN 2050-084X

e-ISSN 2050-084X

Journal eLife

Quellenangaben Volume: 11, Article Number: e81217

Publisher eLife Sciences Publications

Publishing Place Sheraton House, Castle Park, Cambridge, Cb3 0ax, England

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute of AI for Health (AIH)

Grants Shanghai Municipal Science and Technology Major Project
Fudan University the Office of Global Partnerships (Key Projects Development Fund)
National Natural Science Foundation of China