Title: 3D SA-UNet: 3D Spatial Attention UNet with 3D Atrous Spatial Pyramid Pooling for White Matter Hyperintensities Segmentation Show Chinese title

Title: 3D SA-UNet：具有3D空洞空间金字塔池化的3D空间注意力UNet用于白质高信号分割

Abstract: 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. Show Chinese abstract

Abstract: 白质高信号（WMH）是一种与阿尔茨海默病和中风等多种疾病相关的影像学特征。 使用计算机技术准确分割WMH对于早期疾病诊断至关重要。 然而，由于图像中的病变较小、对比度低且高度不连续，包含有限的上下文和空间信息，因此该任务仍然具有挑战性。 为了解决这一挑战，我们提出了一种称为3D空间注意力U-Net（3D SA-UNet）的深度学习模型，仅使用液体衰减反转恢复（FLAIR）扫描进行自动WMH分割。 3D SA-UNet引入了一个3D空间注意力模块，可以突出重要的病变特征，如WMH，同时抑制不重要的区域。 此外，为了捕捉不同尺度的特征，我们将空洞空间金字塔池化（ASPP）模块扩展为3D版本，从而增强了网络的分割性能。 我们在公开可用的数据集上评估了我们的方法，并证明了3D空间注意力模块和3D ASPP在WMH分割中的有效性。 通过实验结果，已经证明我们提出的3D SA-UNet模型相比其他最先进的3D卷积神经网络具有更高的准确性。