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The vascular structure of blood vessels is important in diagnosing retinalconditions such as glaucoma and diabetic retinopathy. Accurate segmentation ofthese vessels can help in detecting retinal objects such as the optic disc andoptic cup and hence determine if there are damages to these areas. Moreover,the structure of the vessels can help in diagnosing glaucoma. The rapiddevelopment of digital imaging and computer-vision techniques has increased thepotential for developing approaches for segmenting retinal vessels. In thispaper, we propose an approach for segmenting retinal vessels that uses deeplearning along with transfer learning. We adapted the U-Net structure to use acustomized InceptionV3 as the encoder and used multiple skip connections toform the decoder. Moreover, we used a weighted loss function to handle theissue of class imbalance in retinal images. Furthermore, we contributed a newdataset to this field. We tested our approach on six publicly availabledatasets and a newly created dataset. We achieved an average accuracy of 95.60%and a Dice coefficient of 80.98%. The results obtained from comprehensiveexperiments demonstrate the robustness of our approach to the segmentation ofblood vessels in retinal images obtained from different sources. Our approachresults in greater segmentation accuracy than other approaches.

Transfer Learning Through Weighted Loss Function and Group Normalization for Vessel Segmentation from Retinal Images