White Matter Hyperintensity (WMH) is an imaging feature related to various diseases such as dementia and stroke. Accurately segmenting WMH using computer technology is crucial for early disease diagnosis. However, this task remains challenging due to the small lesions with low contrast and high discontinuity in the images, which contain limited contextual and spatial information. To address this challenge, we propose a deep learning model called 3D Spatial Attention U-Net (3D SA-UNet) for automatic WMH segmentation using only Fluid Attenuation Inversion Recovery (FLAIR) scans. The 3D SA-UNet introduces a 3D Spatial Attention Module that highlights important lesion features, such as WMH, while suppressing unimportant regions. Additionally, to capture features at different scales, we extend the Atrous Spatial Pyramid Pooling (ASPP) module to a 3D version, enhancing the segmentation performance of the network. We evaluate our method on publicly available dataset and demonstrate the effectiveness of 3D spatial attention module and 3D ASPP in WMH segmentation. Through experimental results, it has been demonstrated that our proposed 3D SA-UNet model achieves higher accuracy compared to other state-of-the-art 3D convolutional neural networks.

翻译：摘要：白质高信号（WMH）是与痴呆、脑卒中等多种疾病相关的影像学特征。利用计算机技术精确分割WMH对疾病的早期诊断至关重要。然而，由于病灶体积小、对比度低且图像中存在高度不连续性，导致可用的上下文与空间信息有限，这一任务仍具有挑战性。为解决此问题，我们提出了一种名为3D空间注意力U-Net（3D SA-UNet）的深度学习模型，仅利用液体衰减反转恢复（FLAIR）扫描实现WMH自动分割。该模型引入3D空间注意力模块，可突出显示WMH等重要病灶特征，同时抑制无关区域。此外，为捕捉多尺度特征，我们将空洞空间金字塔池化（ASPP）模块扩展为3D版本，提升了网络的分割性能。我们在公开数据集上评估了所提方法，验证了3D空间注意力模块与3D ASPP在WMH分割中的有效性。实验结果表明，与其它先进的3D卷积神经网络相比，所提出的3D SA-UNet模型实现了更高的精度。