Segmentation of fundus vascular images based on a dual-attention mechanism

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.