论文标题
SA-UNET：视网膜血管分割的空间注意U-NET
SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation
论文作者
论文摘要
视网膜血管的精确分割对于早期诊断糖尿病和高血压等眼睛相关疾病的早期诊断具有重要意义。在这项工作中，我们提出了一个名为“空间注意U-NET（SA-UNET”）的轻型网络，该网络不需要数千个注释的培训样本，并且可以以数据增强方式使用以更有效地使用可用的注释样本。 SA-UNET引入了空间注意模块，该模块沿空间维度呈现注意力图，并将注意力图乘以输入特征映射以进行自适应特征的细化。此外，提议的网络还采用结构化的辍学卷积块，而不是U-NET的原始卷积块，以防止网络过度拟合。我们根据两个基准视网膜数据集评估SA-UNET：血管提取（驱动器）数据集和儿童心脏和健康研究（Chase_DB1）数据集。结果表明，所提出的SA-UNET在两个数据集上都达到了最先进的性能。
The precise segmentation of retinal blood vessels is of great significance for early diagnosis of eye-related diseases such as diabetes and hypertension. In this work, we propose a lightweight network named Spatial Attention U-Net (SA-UNet) that does not require thousands of annotated training samples and can be utilized in a data augmentation manner to use the available annotated samples more efficiently. SA-UNet introduces a spatial attention module which infers the attention map along the spatial dimension, and multiplies the attention map by the input feature map for adaptive feature refinement. In addition, the proposed network employs structured dropout convolutional blocks instead of the original convolutional blocks of U-Net to prevent the network from overfitting. We evaluate SA-UNet based on two benchmark retinal datasets: the Vascular Extraction (DRIVE) dataset and the Child Heart and Health Study (CHASE_DB1) dataset. The results show that the proposed SA-UNet achieves state-of-the-art performance on both datasets.The implementation and the trained networks are available on Github1.
