ArticleNeuroImage2021
U-net model for brain extraction: Trained on humans for transfer to non-human primates.
Article in NeuroImage, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Brainana: an end-to-end preprocessing framework for macaque neuroimaging.bioRxiv : the preprint server for biology · 2026Article
- Deep brain stimulation induces white matter remodeling and functional changes to brain-wide networks.Nature neuroscience · 2026Article
- Bridging Species with AI: A Cross-Species Deep Learning Model for Fracture Detection and Beyond.Bioengineering (Basel, Switzerland) · 2026Article
- 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
- Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging.Magnetic resonance in medicine · 2025Review
- A role for the insula in establishing social dominance: structural and functional MRI studies in nonhuman primates.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Pharmacological Modulation of Dopamine Receptors Reveals Distinct Brain-Wide Networks Associated with Learning and Motivation in Nonhuman Primates.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Artificial intelligence based surgical support for experimental laparoscopic Nissen fundoplication.Frontiers in pediatrics · 2025Article
- Modulation and distribution of extracellular free water and tract deficits in rhesus macaques before and after the initiation of emtricitabine + tenofovir disoproxil fumarate + dotutegravir treatment.Frontiers in immunology · 2025Article
- Pharmacological modulation of dopamine receptors reveals distinct brain-wide networks associated with learning and motivation in non-human primates.bioRxiv : the preprint server for biology · 2024Article
- Evaluation of registration-based vs. manual segmentation of rhesus macaque brain MRIs.Brain structure & function · 2024Article
- The neural basis of resting-state fMRI functional connectivity in fronto-limbic circuits revealed by chemogenetic manipulation.Nature communications · 2024Article
- Initiating PeriCBD to probe perinatal influences on neurodevelopment during 3-10 years in China.Scientific data · 2024Article
- Diffusion MRI with Machine Learning.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- MEBRAINS 1.0: A new population-based macaque atlas.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Brain tumor image segmentation based on improved FPN.BMC medical imaging · 2023Article
- Striatonigrostriatal connectivity-based cross-species parcellation of human and macaque substantia nigra.Human brain mapping · 2023Article
- Mapping cross-species connectome atlas of human and macaque striatum.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- versaFlow: a versatile pipeline for resolution adapted diffusion MRI processing and its application to studying the variability of the PRIME-DE database.Frontiers in neuroinformatics · 2023Article
- HC-Net: A hybrid convolutional network for non-human primate brain extraction.Frontiers in computational neuroscience · 2023Article
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Authors and funding
20 authors.
Funding
Abstract
Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20 s~10 min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.