ArticleFrontiers in neuroinformatics2017
Automated Ischemic Lesion Segmentation in MRI Mouse Brain Data after Transient Middle Cerebral Artery Occlusion.
Article in Frontiers in neuroinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 24 citations in OpenAlex.
- Estimation of the Ischemic Lesion in the Experimental Stroke Studies Using Magnetic Resonance Imaging (Review).Bulletin of experimental biology and medicine · 2024Review
- Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images.Scientific reports · 2023Article
- Reactivation of corticogenesis-related transcriptional factors BCL11B and SATB2 after ischemic lesion of the adult mouse brain.Scientific reports · 2023Article
- RU-Net: skull stripping in rat brain MR images after ischemic stroke with rat U-Net.BMC medical imaging · 2023Article
- Automatic Cerebral Hemisphere Segmentation in Rat MRI with Ischemic Lesions via Attention-based Convolutional Neural Networks.Neuroinformatics · 2023Article
- Machine learning based analysis of stroke lesions on mouse tissue sections.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2022Article
- Serum Exosomal microRNA-27-3p Aggravates Cerebral Injury and Inflammation in Patients with Acute Cerebral Infarction by Targeting PPARγ.Inflammation · 2021Article
- Impact of hydroxytyrosol on stroke: tracking therapy response on neuroinflammation and cerebrovascular parameters using PET-MR imaging and on functional outcomes.Theranostics · 2021Article
- Machine learning identifies stroke features between species.Theranostics · 2021Article
- RatLesNetv2: A Fully Convolutional Network for Rodent Brain Lesion Segmentation.Frontiers in neuroscience · 2020Article
- IndividualFrontiers in immunology · 2019Article
- A Heavy Tailed Expectation Maximization Hidden Markov Random Field Model with Applications to Segmentation of MRI.Frontiers in neuroinformatics · 2017Article
- MRI Mouse Brain Data of Ischemic Lesion after Transient Middle Cerebral Artery Occlusion.Frontiers in neuroinformatics · 2017Article
- A robust and reproducible automated MRI pipeline for quantifying tissue outcomes after experimental stroke in multi-center preclinical networks.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Magnetic resonance imaging (MRI) has become increasingly important in ischemic stroke experiments in mice, especially because it enables longitudinal studies. Still, quantitative analysis of MRI data remains challenging mainly because segmentation of mouse brain lesions in MRI data heavily relies on time-consuming manual tracing and thresholding techniques. Therefore, in the present study, a fully automated approach was developed to analyze longitudinal MRI data for quantification of ischemic lesion volume progression in the mouse brain. We present a level-set-based lesion segmentation algorithm that is built using a minimal set of assumptions and requires only one MRI sequence (T2) as input. To validate our algorithm we used a heterogeneous data set consisting of 121 mouse brain scans of various age groups and time points after infarct induction and obtained using different MRI hardware and acquisition parameters. We evaluated the volumetric accuracy and regional overlap of ischemic lesions segmented by our automated method against the ground truth obtained in a semi-automated fashion that includes a highly time-consuming manual correction step. Our method shows good agreement with human observations and is accurate on heterogeneous data, whilst requiring much shorter average execution time. The algorithm developed here was compiled into a toolbox and made publically available, as well as all the data sets.
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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.