Xu Bin, Zhang Xiaofeng, Tian Congyu, Yan Wei, Wang Yuanqing, Zhang Doudou, Liao Xiangyun, Cai Xiaodong
Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China.
Shenzhen University School of Medicine, Shenzhen, Guangdong, China.
Front Neurol. 2023 Jul 27;14:1242685. doi: 10.3389/fneur.2023.1242685. eCollection 2023.
Cerebral white matter hyperintensity can lead to cerebral small vessel disease, MRI images in the brain are used to assess the degree of pathological changes in white matter regions. In this paper, we propose a framework for automatic 3D segmentation of brain white matter hyperintensity based on MRI images to address the problems of low accuracy and segmentation inhomogeneity in 3D segmentation. We explored correlation analyses of cognitive assessment parameters and multiple comparison analyses to investigate differences in brain white matter hyperintensity volume among three cognitive states, Dementia, MCI and NCI. The study explored the correlation between cognitive assessment coefficients and brain white matter hyperintensity volume.
This paper proposes an automatic 3D segmentation framework for white matter hyperintensity using a deep multi-mapping encoder-decoder structure. The method introduces a 3D residual mapping structure for the encoder and decoder. Multi-layer Cross-connected Residual Mapping Module (MCRCM) is proposed in the encoding stage to enhance the expressiveness of model and perception of detailed features. Spatial Attention Weighted Enhanced Supervision Module (SAWESM) is proposed in the decoding stage to adjust the supervision strategy through a spatial attention weighting mechanism. This helps guide the decoder to perform feature reconstruction and detail recovery more effectively.
Experimental data was obtained from a privately owned independent brain white matter dataset. The results of the automatic 3D segmentation framework showed a higher segmentation accuracy compared to nnunet and nnunet-resnet, with a -value of <0.001 for the two cognitive assessment parameters MMSE and MoCA. This indicates that larger brain white matter are associated with lower scores of MMSE and MoCA, which in turn indicates poorer cognitive function. The order of volume size of white matter hyperintensity in the three groups of cognitive states is dementia, MCI and NCI, respectively.
The paper proposes an automatic 3D segmentation framework for brain white matter that achieves high-precision segmentation. The experimental results show that larger volumes of segmented regions have a negative correlation with lower scoring coefficients of MMSE and MoCA. This correlation analysis provides promising treatment prospects for the treatment of cerebral small vessel diseases in the brain through 3D segmentation analysis of brain white matter. The differences in the volume of white matter hyperintensity regions in subjects with three different cognitive states can help to better understand the mechanism of cognitive decline in clinical research.
脑白质高信号可导致脑小血管病，脑部MRI图像用于评估白质区域的病理变化程度。在本文中，我们提出了一种基于MRI图像的脑白质高信号自动三维分割框架，以解决三维分割中准确性低和分割不均匀的问题。我们进行了认知评估参数的相关性分析和多重比较分析，以研究痴呆、轻度认知障碍（MCI）和正常认知（NCI）三种认知状态下脑白质高信号体积的差异。该研究探讨了认知评估系数与脑白质高信号体积之间的相关性。
本文提出了一种使用深度多映射编码器-解码器结构的白质高信号自动三维分割框架。该方法在编码器和解码器中引入了三维残差映射结构。在编码阶段提出了多层交叉连接残差映射模块（MCRCM），以增强模型的表现力和对细节特征的感知。在解码阶段提出了空间注意力加权增强监督模块（SAWESM），通过空间注意力加权机制调整监督策略。这有助于指导解码器更有效地进行特征重建和细节恢复。
实验数据来自一个私有独立脑白质数据集。自动三维分割框架的结果显示，与nnunet和nnunet-resnet相比，分割精度更高，两个认知评估参数MMSE和MoCA的p值<0.001。这表明更大的脑白质与更低的MMSE和MoCA评分相关，进而表明认知功能更差。三种认知状态下白质高信号的体积大小顺序分别为痴呆、MCI和NCI。
本文提出了一种用于脑白质的自动三维分割框架，实现了高精度分割。实验结果表明，分割区域体积越大与MMSE和MoCA评分系数越低呈负相关。这种相关性分析为通过脑白质的三维分割分析治疗脑部脑小血管疾病提供了有前景的治疗前景。三种不同认知状态受试者白质高信号区域体积的差异有助于在临床研究中更好地理解认知衰退的机制。
