Accurately segmenting blood vessels in retinal fundus images is crucial in the early screening, diagnosing, and evaluating some ocular diseases. However, significant light variations and non-uniform contrast in these images make segmentation quite challenging. Thus, this paper employ an attention fusion mechanism that combines the channel attention and spatial attention mechanisms constructed by Transformer to extract information from retinal fundus images in both spatial and channel dimensions. To eliminate noise from the encoder image, a spatial attention mechanism is introduced in the skip connection. Moreover, a Dropout layer is employed to randomly discard some neurons, which can prevent overfitting of the neural network and improve its generalization performance. Experiments were conducted on publicly available datasets DERIVE, STARE, and CHASEDB1. The results demonstrate that our method produces satisfactory results compared to some recent retinal fundus image segmentation algorithms.

翻译：准确分割视网膜眼底图像中的血管对于某些眼部疾病的早期筛查、诊断和评估至关重要。然而，这些图像中显著的光照变化和非均匀对比度使得分割极具挑战性。因此，本文采用一种注意力融合机制，该机制结合了由Transformer构建的通道注意力与空间注意力机制，从视网膜眼底图像的空间维度和通道维度提取信息。为了消除编码器图像中的噪声，在跳跃连接中引入了空间注意力机制。此外，采用Dropout层随机丢弃部分神经元，以防止神经网络过拟合并提升其泛化性能。在公开数据集DERIVE、STARE和CHASEDB1上进行了实验。结果表明，与近期的一些视网膜眼底图像分割算法相比，本文方法取得了令人满意的结果。