Computational Biomedical Engineering Laboratory (CBEL), Department of Engineering Science and Ocean Engineering, National Taiwan University, No. 1 Sec. 4 Roosevelt Road, Daan, Taipei, 10617, Taiwan.
Department of Neurology and Stroke Center, National Taiwan University Hospital, Taipei, 10002, Taiwan.
BMC Med Imaging. 2023 Mar 27;23(1):44. doi: 10.1186/s12880-023-00994-8.
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).
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.
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.
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.
实验性缺血性中风模型在解释脑缺血机制和评估病理程度发展方面起着重要作用。磁共振成像（MRI）大鼠脑图像具有准确、自动的颅骨剥离工具对于实验性中风分析至关重要。由于缺乏可靠的大鼠脑分割方法，并且受到临床前研究的需求的推动，本文开发了一种新的颅骨剥离算法，用于提取中风后大鼠脑磁共振图像的区域，命名为 Rat U-Net（RU-Net）。
基于 U 形深度学习架构，该框架将批量归一化与残差网络集成在一起，以实现高效的端到端分割。利用编码器和解码器之间的池索引传输机制来增强空间相关性。两个不同的弥散加权成像（DWI）和 T2 加权磁共振成像（T2WI）模态对应于两个内部数据集，每个数据集由 55 个对象组成，用于评估所提出的 RU-Net 的性能。
广泛的实验表明，在多样化的大鼠脑磁共振图像中具有出色的分割精度。建议我们的大鼠颅骨剥离网络优于几种最先进的方法，并在 DWI 和 T2WI 图像数据集上分别获得了 98.04%（p<0.001）和 97.67%（p<0.001）的平均 Dice 分数。
所提出的 RU-Net 有望推进临床前中风研究，并为病理大鼠脑图像提取提供有效的工具，其中大鼠脑区域的准确分割是基础。
