ArticleFrontiers in neuroscience2021
3D U-Net Improves Automatic Brain Extraction for Isotropic Rat Brain Magnetic Resonance Imaging Data.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 29 citations in OpenAlex.
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- Automated brain extraction for canine magnetic resonance images.BMC veterinary research · 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
- 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
- Automatic Adaptive Algorithm for Delineation of Cerebral-Spinal Fluid Regions for Non-contrast Magnetic Resonance Imaging Volumetry and Cisternography in Mice.Bio-protocol · 2025Article
- Automatic Segmentation of the Cisternal Segment of Trigeminal Nerve on MRI Using Deep Learning.International journal of biomedical imaging · 2025Article
- 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
- Accuracy of skull stripping in a single-contrast convolutional neural network model using eight-contrast magnetic resonance images.Radiological physics and technology · 2023Article
- Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images.Scientific reports · 2023Article
- AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study.Acta neuropathologica communications · 2023Article
- Body Location Embedded 3D U-Net (BLE-U-Net) for Ovarian Cancer Ascites Segmentation on CT scans.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Clinical Decision Support Framework for Segmentation and Classification of Brain Tumor MRIs Using a U-Net and DCNN Cascaded Learning Algorithm.Healthcare (Basel, Switzerland) · 2022Article
- Ensemble learning based on efficient features combination can predict the outcome of recurrence-free survival in patients with hepatocellular carcinoma within three years after surgery.Frontiers in oncology · 2022Article
- Open-source software to calculate the static sciatic index automatically.Regenerative medicineArticle
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
Brain extraction is a critical pre-processing step in brain magnetic resonance imaging (MRI) analytical pipelines. In rodents, this is often achieved by manually editing brain masks slice-by-slice, a time-consuming task where workloads increase with higher spatial resolution datasets. We recently demonstrated successful automatic brain extraction via a deep-learning-based framework, U-Net, using 2D convolutions. However, such an approach cannot make use of the rich 3D spatial-context information from volumetric MRI data. In this study, we advanced our previously proposed U-Net architecture by replacing all 2D operations with their 3D counterparts and created a 3D U-Net framework. We trained and validated our model using a recently released CAMRI rat brain database acquired at isotropic spatial resolution, including T2-weighted turbo-spin-echo structural MRI and T2*-weighted echo-planar-imaging functional MRI. The performance of our 3D U-Net model was compared with existing rodent brain extraction tools, including Rapid Automatic Tissue Segmentation, Pulse-Coupled Neural Network, SHape descriptor selected External Regions after Morphologically filtering, and our previously proposed 2D U-Net model. 3D U-Net demonstrated superior performance in Dice, Jaccard, center-of-mass distance, Hausdorff distance, and sensitivity. Additionally, we demonstrated the reliability of 3D U-Net under various noise levels, evaluated the optimal training sample sizes, and disseminated all source codes publicly, with a hope that this approach will benefit rodent MRI research community.
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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.