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.

基于 IRSLO 图像，我们研发了一种专门用于血管分割的算法，并将其作为开源项目发布。该算法采用了 U-Net 深度学习网络，在测试集上达到了较高的准确率，并可用于计算血管指标，如分形维度和血管密度。