  Retinal imaging has emerged as a promising method of addressing this
challenge, taking advantage of the unique structure of the retina. The retina
is an embryonic extension of the central nervous system, providing a direct in
vivo window into neurological health. Recent studies have shown that specific
structural changes in retinal vessels can not only serve as early indicators of
various diseases but also help to understand disease progression. In this work,
we present a lightweight retinal vessel segmentation network based on the
encoder-decoder mechanism with region-guided attention. We introduce inverse
addition attention blocks with region guided attention to focus on the
foreground regions and improve the segmentation of regions of interest. To
further boost the model's performance on retinal vessel segmentation, we employ
a weighted dice loss. This choice is particularly effective in addressing the
class imbalance issues frequently encountered in retinal vessel segmentation
tasks. Dice loss penalises false positives and false negatives equally,
encouraging the model to generate more accurate segmentation with improved
object boundary delineation and reduced fragmentation. Extensive experiments on
a benchmark dataset show better performance (0.8285, 0.8098, 0.9677, and 0.8166
recall, precision, accuracy and F1 score respectively) compared to
state-of-the-art methods.
