ArticleFrontiers in neuroscience2020
Automatic Skull Stripping of Rat and Mouse Brain MRI Data Using U-Net.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 62 citations in OpenAlex.
- Article
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- PIGSKIN: PIG SKull stripping with synthetic Images and Neural networks.AI in neuroscience · 2026Article
- Exploring the Feasibility of Zero-Shot Super-Resolution in Preclinical Imaging.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Multimodal MR imaging for quantification of brain lipid in mice at 9.4T.Journal of translational medicine · 2025Article
- Psychedelic compounds directly excite 5-HTTranslational psychiatry · 2025Article
- Multimodal MR Imaging for quantification of brain lipid in mice at 9.4T.bioRxiv : the preprint server for biology · 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
- Multimodal neuroimaging ofFrontiers in neuroscience · 2025Article
- Mouse Brain Extractor: Brain segmentation of mouse MRI using global positional encoding and SwinUNETR.bioRxiv : the preprint server for biology · 2024Article
- Fully automated whole brain segmentation from rat MRI scans with a convolutional neural network.Journal of neuroscience methods · 2024Article
- Estimation of the Ischemic Lesion in the Experimental Stroke Studies Using Magnetic Resonance Imaging (Review).Bulletin of experimental biology and medicine · 2024Review
- Acute alcohol induces greater dose-dependent increase in the lateral cortical network functional connectivity in adult than adolescent rats.Addiction neuroscience · 2023Article
- Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images.Scientific reports · 2023Article
- MR Template-Based Individual Brain PET Volumes-of-Interest Generation Neither Using MR nor Using Spatial Normalization.Nuclear medicine and molecular imaging · 2023Article
- RU-Net: skull stripping in rat brain MR images after ischemic stroke with rat U-Net.BMC medical imaging · 2023Article
- Neuronal dynamics of the default mode network and anterior insular cortex: Intrinsic properties and modulation by salient stimuli.Science advances · 2023Article
- AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study.Acta neuropathologica communications · 2023Article
- Automated identification of piglet brain tissue from MRI images using Region-based Convolutional Neural Networks.PloS one · 2023Article
- Automatic Cerebral Hemisphere Segmentation in Rat MRI with Ischemic Lesions via Attention-based Convolutional Neural Networks.Neuroinformatics · 2023Article
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Authors and funding
12 authors at 2 institutions in 2 countries.
Funding
Abstract
Accurate removal of magnetic resonance imaging (MRI) signal outside the brain, a.k.a., skull stripping, is a key step in the brain image pre-processing pipelines. In rodents, this is mostly achieved by manually editing a brain mask, which is time-consuming and operator dependent. Automating this step is particularly challenging in rodents as compared to humans, because of differences in brain/scalp tissue geometry, image resolution with respect to brain-scalp distance, and tissue contrast around the skull. In this study, we proposed a deep-learning-based framework, U-Net, to automatically identify the rodent brain boundaries in MR images. The U-Net method is robust against inter-subject variability and eliminates operator dependence. To benchmark the efficiency of this method, we trained and validated our model using both in-house collected and publicly available datasets. In comparison to current state-of-the-art methods, our approach achieved superior averaged Dice similarity coefficient to ground truth T2-weighted rapid acquisition with relaxation enhancement and T2
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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.