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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Vydáno v:	BMC medical imaging Ročník 23; číslo 1; s. 44 - 14
Hlavní autoři:	Chang, Herng-Hua, Yeh, Shin-Joe, Chiang, Ming-Chang, Hsieh, Sung-Tsang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 27.03.2023 BioMed Central Ltd Springer Nature B.V BMC
Témata:	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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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2342 1471-2342
DOI:	10.1186/s12880-023-00994-8