July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
Artery and Vein Segmentation in Retinal Oximetry Images Using Convolutional Neural Networks
Author Affiliations & Notes
  • Robert Arnar Karlsson
    Institute of Physiology, University of Iceland, Reykjavik, Iceland
    Electrical and Computer Engineering, University of Iceland, Iceland
  • Sveinn Hákon Hardarson
    Institute of Physiology, University of Iceland, Reykjavik, Iceland
  • Footnotes
    Commercial Relationships   Robert Karlsson, Oxymap ehf. (P), Oxymap ehf. (C), Oxymap ehf. (I); Sveinn Hardarson, Oxymap ehf (I)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 1502. doi:
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    • Get Citation

      Robert Arnar Karlsson, Sveinn Hákon Hardarson; Artery and Vein Segmentation in Retinal Oximetry Images Using Convolutional Neural Networks. Invest. Ophthalmol. Vis. Sci. 2019;60(9):1502.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Recent studies have shown that retinal oximetry could assist in the management of diseases such as diabetic retinopathy, central retinal vein occlusion, glaucoma and Alzheimer's disease.
In order for retinal oximetry to be automated, the retinal vessels must be segmented and classified into arteries and veins.
Here we demonstrate the use of convolutional neural networks on images from the OxymapT1 retinal oximeter to segment the retinal image into arteries and veins.

Methods : Retinal oximetry images were obtained from 56 individuals who were healthy or suffered from a pathology of the eye with only one image from each individual being used. Each image was manually segmented into arteries and veins by an expert.
The neural network used for the segmentation task is based on the fully convolutional U-net architecture which has been successfully employed in many biomedical segmentation tasks.
Image preprocessing was applied order to correct for uneven illumination and in order to emphasize the difference between arteries and veins before the images were presented to the network for training. The performance is compared with results from the literature on the commonly used DRIVE dataset.

Results : The proposed convolutional neural network was tested on the tasks of segmenting the retina into vessels and on how well it distinguished between arteries and veins.
The network achieves an accuracy score of 0.987 and an area under the ROC curve of 0.9899 when performing vessel segmentation and an accuracy score of 0.9743 and area under the ROC curve of 0.9919 when classifying vessels into arteries and veins.

Figure 1 compares the performance of the proposed method with results obtained conventional fundus images.
Figure 2 shows example output from the system.

Conclusions : The system presented here performs at least as well in the task of vessel segmentation and separation of vessels into arteries and veins on images from the OxymapT1 as state-of-the-art systems do on the conventional RGB retinal images available through public datasets such as DRIVE.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

 

Comparison with other methods when segmenting into arteries and
veins on the DRIVE dataset versus the proposed method operating on images
from the Oxymap T1.

Comparison with other methods when segmenting into arteries and
veins on the DRIVE dataset versus the proposed method operating on images
from the Oxymap T1.

 

Example classification output for a 512x512 pixel patch. The input image is shown to the left, the target segmentation mask is in the middle and the output of the classifier is to the right.

Example classification output for a 512x512 pixel patch. The input image is shown to the left, the target segmentation mask is in the middle and the output of the classifier is to the right.

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