RU-Net: skull stripping in rat brain MR images after ischemic stroke with rat U-Net

Background Experimental 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 crucia...

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Veröffentlicht in:BMC medical imaging Jg. 23; H. 1; S. 44 - 14
Hauptverfasser: Chang, Herng-Hua, Yeh, Shin-Joe, Chiang, Ming-Chang, Hsieh, Sung-Tsang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 27.03.2023
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Algorithms
Animal experimentation
Animals
Brain
Brain - diagnostic imaging
Brain segmentation
Coders
Datasets
Deep learning
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Imaging
Ischemia
Ischemic Stroke
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Medical imaging
Medicine
Medicine & Public Health
MRI
Neural networks
Neuroimaging
Radiology
Rats
Rodents
Skull
Skull stripping
Stroke
Stroke (Disease)
Stroke - diagnostic imaging
Tomography
U-Net
Deep learning
Brain segmentation
Skull stripping
Ischemic stroke
MRI
U-Net
ISSN:1471-2342, 1471-2342
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Zusammenfassung:Background Experimental 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). Methods Based 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. Results Extensive 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. Conclusion The 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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ISSN:1471-2342
1471-2342
DOI:10.1186/s12880-023-00994-8