A publicly available vessel segmentation algorithm for SLO images
Adam Threlfall Samuel Gibbon James Cameron Tom MacGillivray
Background and Objective: Infra-red scanning laser ophthalmoscope (IRSLO)
images are akin to colour fundus photographs in displaying the posterior pole
and retinal vasculature fine detail. While there are many trained networks
readily available for retinal vessel segmentation in colour fundus photographs,
none cater to IRSLO images. Accordingly, we aimed to develop (and release as
open source) a vessel segmentation algorithm tailored specifically to IRSLO
images. Materials and Methods: We used 23 expertly annotated IRSLO images from
the RAVIR dataset, combined with 7 additional images annotated in-house. We
trained a U-Net (convolutional neural network) to label pixels as 'vessel' or
'background'. Results: On an unseen test set (4 images), our model achieved an
AUC of 0.981, and an AUPRC of 0.815. Upon thresholding, it achieved a
sensitivity of 0.844, a specificity of 0.983, and an F1 score of 0.857.
Conclusion: We have made our automatic segmentation algorithm publicly
available and easy to use. Researchers can use the generated vessel maps to
compute metrics such as fractal dimension and vessel density.