Annals of Computer Science and Information Systems, Volume 15

DOI: http://dx.doi.org/10.15439/2018F127

Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 117–123 (2018)

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Abstract. ��Fundus images are one of the main methods for diagnosing eye diseases in modern medicine. The vascular segmentation of fundus images is an essential step in quantitative disease analysis. Based on the previous studies, we found that the category imbalance is one of the main reasons that restrict the improvement of segmentation accuracy. This paper presents a new method for supervised retinal vessel segmentation that can effectively solve the above problems. In recent years, it is a popular method that using deep learning to solve retinal vessel segmentation. We have improved the loss function for deep learning in order to better handle category imbalances. By using a multi-scale convolutional neural network structure and label processing approach, our results have reached the most advanced level. Our approach is a meaningful attempt to improve blood vessel segmentation and further improve the diagnostic level of eye diseases.

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