  Accurately segmenting blood vessels in retinal fundus images is crucial in
the early screening, diagnosing, and evaluating some ocular diseases, yet it
poses a nontrivial uncertainty for the segmentation task due to various factors
such as significant light variations, uneven curvilinear structures, and
non-uniform contrast. As a result, a multiple attention-guided fusion network
(MAF-Net) is proposed to accurately detect blood vessels in retinal fundus
images. Currently, traditional UNet-based models may lose partial information
due to explicitly modeling long-distance dependencies, which may lead to
unsatisfactory results. To enrich contextual information for the loss of scene
information compensation, an attention fusion mechanism that combines the
channel attention with spatial attention mechanisms constructed by Transformer
is employed to extract various features of blood vessels from retinal fundus
images. Subsequently, a unique spatial attention mechanism is applied in the
skip connection to filter out redundant information and noise from low-level
features, thus enabling better integration with high-level features. In
addition, a DropOut layer is employed to randomly discard some neurons, which
can prevent overfitting of the deep learning network and improve its
generalization performance. Experimental results were verified in public
datasets DRIVE, STARE and CHASEDB1 with F1 scores of 0.818, 0.836 and 0.811,
and Acc values of 0.968, 0.973 and 0.973, respectively. Both visual inspection
and quantitative evaluation demonstrate that our method produces satisfactory
results compared to some state-of-the-art methods.
