Retinal Vessel Segmentation Based on the Anam-Net Model

Khursheed Aurangzeb, Syed Irtaza Haider, Musaed Alhussein

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)54-64
Number of pages11
JournalElektronika ir Elektrotechnika
Issue number3
StatePublished - Jun 28 2022
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-07-05
Acknowledgements: For computer time, this research used the resources of the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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