ArticleBMC medical imaging2023
RU-Net: skull stripping in rat brain MR images after ischemic stroke with rat U-Net.
Article in BMC medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Flood change detection model based on an improved U-net network and multi-head attention mechanism.Scientific reports · 2025Article
- Deep learning segmentation model for quantification of infarct size in pigs with myocardial ischemia/reperfusion.Basic research in cardiology · 2024Article
- Fully automated whole brain segmentation from rat MRI scans with a convolutional neural network.Journal of neuroscience methods · 2024Article
- Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images.Scientific reports · 2023Article
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Abstract
backgroundExperimental ischemic stroke models play a fundamental role in interpreting the mechanism of cerebral ischemia and appraising the development of pathological extent. An accurate and automatic skull stripping tool for rat brain image volumes with magnetic resonance imaging (MRI) are crucial in experimental stroke analysis. Due to the deficiency of reliable rat brain segmentation methods and motivated by the demand for preclinical studies, this paper develops a new skull stripping algorithm to extract the rat brain region in MR images after stroke, which is named Rat U-Net (RU-Net).
methodsBased on a U-shape like deep learning architecture, the proposed framework integrates batch normalization with the residual network to achieve efficient end-to-end segmentation. A pooling index transmission mechanism between the encoder and decoder is exploited to reinforce the spatial correlation. Two different modalities of diffusion-weighted imaging (DWI) and T2-weighted MRI (T2WI) corresponding to two in-house datasets with each consisting of 55 subjects were employed to evaluate the performance of the proposed RU-Net.
resultsExtensive experiments indicated great segmentation accuracy across diversified rat brain MR images. It was suggested that our rat skull stripping network outperformed several state-of-the-art methods and achieved the highest average Dice scores of 98.04% (p < 0.001) and 97.67% (p < 0.001) in the DWI and T2WI image datasets, respectively.
conclusionThe proposed RU-Net is believed to be potential for advancing preclinical stroke investigation and providing an efficient tool for pathological rat brain image extraction, where accurate segmentation of the rat brain region is fundamental.
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