Exploring The Limits Of Data Augmentation For Retinal Vessel Segmentation

Retinal Vessel Segmentation is important for the diagnosis of various diseases. The research on retinal vessel segmentation focuses mainly on the improvement of the segmentation model which is usually based on U-Net architecture. In our study, we use the U-Net architecture and we rely on heavy data...

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Bibliographic Details
Published in:arXiv.org 2021-05
Main Authors: Uysal, Enes Sadi, M \c{S}afak Bilici, B Selin Zaza, Özgenç, M Yiğit, Boyar, Onur
Format: Article
Language:English
Subjects:
Blood vessels
Data augmentation
Image segmentation
Medical imaging
Online Access:Get full text
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Summary:Retinal Vessel Segmentation is important for the diagnosis of various diseases. The research on retinal vessel segmentation focuses mainly on the improvement of the segmentation model which is usually based on U-Net architecture. In our study, we use the U-Net architecture and we rely on heavy data augmentation in order to achieve better performance. The success of the data augmentation relies on successfully addressing the problem of input images. By analyzing input images and performing the augmentation accordingly we show that the performance of the U-Net model can be increased dramatically. Results are reported using the most widely used retina dataset, DRIVE.
ISSN:2331-8422