Retinal Vessel Segmentation Based on the Anam-Net Model

Authors

  • Khursheed Aurangzeb Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Saudi Arabia
  • Syed Irtaza Haider College of Computer and Information Sciences, King Saud University, Saudi Arabia
  • Musaed Alhussein Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Saudi Arabia

DOI:

https://doi.org/10.5755/j02.eie.30594

Keywords:

Anam-Net, Deep learning, Data augmentation, Retinal vessel segmentation, Semantic segmentation

Abstract

Accurate segmentation of retinal blood vessels can help ophthalmologists diagnose eye-related diseases such as diabetes and hypertension. The task of segmentation of the vessels comes with a number of challenges. Some of the challenges are due to haemorrhages and microaneurysms in fundus imaging, while others are due to the central vessel reflex and low contrast. Encoder-decoder networks have recently achieved excellent performance in retinal vascular segmentation at the trade-off of increased computational complexity. In this work, we use the Anam-Net model to accurately segment retinal vessels at a low computational cost. The Anam-Net model consists of a lightweight convolutional neural network (CNN) along with bottleneck layers in the encoder and decoder stages. Compared to the standard U-Net model and the R2U-Net model, the Anam-Net model has 6.9 times and 10.9 times fewer parameters. We evaluated the Anam-Net model on three open-access datasets: DRIVE, STARE, and CHASE_DB. The results show that the Anam-Net model achieves better segmentation accuracy compared to several state-of-the-art methods. For the DRIVE, STARE, and CHASE DB datasets, the model achieved {sensitivity and accuracy} of {0.8601, 0.9660}, {0.8697, 0.9728}, and {0.8553, 0.9746}, respectively. On the DRIVE, STARE, and CHASE_DB datasets, we also conduct cross-training experiments. The outcome of this experiment demonstrates the generalizability and robustness of the Anam-Net model.

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Published

2022-06-28

How to Cite

Aurangzeb, K., Haider, S. I., & Alhussein, M. (2022). Retinal Vessel Segmentation Based on the Anam-Net Model. Elektronika Ir Elektrotechnika, 28(3), 54-64. https://doi.org/10.5755/j02.eie.30594

Issue

Section

SYSTEM ENGINEERING, COMPUTER TECHNOLOGY

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