  The vascular structure of blood vessels is important in diagnosing retinal
conditions such as glaucoma and diabetic retinopathy. Accurate segmentation of
these vessels can help in detecting retinal objects such as the optic disc and
optic cup and hence determine if there are damages to these areas. Moreover,
the structure of the vessels can help in diagnosing glaucoma. The rapid
development of digital imaging and computer-vision techniques has increased the
potential for developing approaches for segmenting retinal vessels. In this
paper, we propose an approach for segmenting retinal vessels that uses deep
learning along with transfer learning. We adapted the U-Net structure to use a
customized InceptionV3 as the encoder and used multiple skip connections to
form the decoder. Moreover, we used a weighted loss function to handle the
issue of class imbalance in retinal images. Furthermore, we contributed a new
dataset to this field. We tested our approach on six publicly available
datasets and a newly created dataset. We achieved an average accuracy of 95.60%
and a Dice coefficient of 80.98%. The results obtained from comprehensive
experiments demonstrate the robustness of our approach to the segmentation of
blood vessels in retinal images obtained from different sources. Our approach
results in greater segmentation accuracy than other approaches.
