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.

翻译：背景与目的：红外扫描激光检眼镜（IRSLO）图像在显示后极部和视网膜血管精细结构方面与彩色眼底照片类似。尽管已有许多适用于彩色眼底照片的视网膜血管分割训练网络，但尚无针对IRSLO图像的专用模型。为此，我们旨在开发（并开源）一种专门面向IRSLO图像的血管分割算法。材料与方法：我们使用了RAVIR数据集中23幅经专家标注的IRSLO图像，并结合内部标注的7幅额外图像。训练了一个U型卷积神经网络（U-Net），将像素标记为“血管”或“背景”。结果：在未见测试集（4幅图像）上，本模型AUC达0.981，AUPRC为0.815。经阈值处理后，敏感性为0.844，特异性为0.983，F1得分为0.857。结论：我们已公开自动分割算法，方便研究人员使用。生成的血管图可用于计算分形维数、血管密度等指标。