ArticleFrontiers in neuroscience2020
RatLesNetv2: A Fully Convolutional Network for Rodent Brain Lesion Segmentation.
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 11 papers.
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11 citing papers in PubMed, 27 citations in OpenAlex.
- Deep Learning and Atlas-Based MRI Segmentation Enable Longitudinal Characterization of Healthy Mouse Brain.Journal of imaging · 2025Article
- Deep learning-driven MRI for accurate brain volumetry in murine models of neurodegenerative diseases.Frontiers in neuroscience · 2025Article
- 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
- Dynamic hierarchical multi-scale fusion network with axial MLP for medical image segmentation.Scientific reports · 2023Article
- Automatic Cerebral Hemisphere Segmentation in Rat MRI with Ischemic Lesions via Attention-based Convolutional Neural Networks.Neuroinformatics · 2023Article
- Article
- Segmentation and differentiation of periventricular and deep white matter hyperintensities in 2D T2-FLAIR MRI based on a cascade U-net.Frontiers in neurology · 2022Article
- Convolutional Neural Networks Enable Robust Automatic Segmentation of the Rat Hippocampus in MRI After Traumatic Brain Injury.Frontiers in neurology · 2022Article
- Identification of White Matter Lesions in Patients With Acute Ischemic Lesions Using U-net.Frontiers in neurology · 2020Article
- RatLesNetv2: A Fully Convolutional Network for Rodent Brain Lesion Segmentation.Frontiers in neuroscience · 2020Article
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a fully convolutional neural network (ConvNet), named RatLesNetv2, for segmenting lesions in rodent magnetic resonance (MR) brain images. RatLesNetv2 architecture resembles an autoencoder and it incorporates residual blocks that facilitate its optimization. RatLesNetv2 is trained end to end on three-dimensional images and it requires no preprocessing. We evaluated RatLesNetv2 on an exceptionally large dataset composed of 916 T2-weighted rat brain MRI scans of 671 rats at nine different lesion stages that were used to study focal cerebral ischemia for drug development. In addition, we compared its performance with three other ConvNets specifically designed for medical image segmentation. RatLesNetv2 obtained similar to higher Dice coefficient values than the other ConvNets and it produced much more realistic and compact segmentations with notably fewer holes and lower Hausdorff distance. The Dice scores of RatLesNetv2 segmentations also exceeded inter-rater agreement of manual segmentations. In conclusion, RatLesNetv2 could be used for automated lesion segmentation, reducing human workload and improving reproducibility. RatLesNetv2 is publicly available at https://github.com/jmlipman/RatLesNetv2.
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