ArticleeLife2022
A generalizable brain extraction net (BEN) for multimodal MRI data from rodents, nonhuman primates, and humans.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Bone Quality Heterogeneity in Mixed Femoral Metastases Revealed by Stimulated Raman Scattering Spectromicroscopy.Annals of biomedical engineering · 2026Article
- Cross-Modality Deep Learning Denoising for Low-Dose μSPECT: Transfer of PET-Trained U‑Net and Diffusion Models.Chemical & biomedical imaging · 2026Article
- Template-Based Label Propagation for Mouse Brain MRI Skull Stripping.Neuroinformatics · 2026Article
- Automated brain extraction for canine magnetic resonance images.BMC veterinary research · 2025Article
- ATM deficiency drives phenotypic diversity and Purkinje cell degeneration in a macaque model of ataxia-telangiectasia.Cell reports. Medicine · 2025Article
- Three-dimensional U-Net with transfer learning improves automated whole brain delineation from MRI brain scans of rats, mice, and monkeys.Computers in biology and medicine · 2025Article
- Deep Learning-Based Retinal Layer Segmentation in Optical Coherence Tomography Scans of Patients with Inherited Retinal Diseases.Klinische Monatsblatter fur Augenheilkunde · 2025Article
- In vivo submillimeter diffusion MRI dataset of 9 macaque brains curated for tractography.Scientific data · 2025Article
- Molecular and functional mapping of the neuroendocrine hypothalamus: a new era begins.Journal of endocrinological investigation · 2024Review
- Age-Related Brain Atrophy and the Positive Effects of Behavioral Enrichment in Middle-Aged Beagles.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images.Scientific reports · 2023Article
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9 authors.
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Abstract
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 4601 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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