A publicly available vessel segmentation algorithm for SLO images
This provides a tool for researchers analyzing retinal vasculature in IRSLO images, but it is incremental as it adapts an existing method to a new data type.
The researchers tackled the lack of vessel segmentation algorithms for infra-red scanning laser ophthalmoscope (IRSLO) images by developing and releasing an open-source U-Net-based model, which achieved an AUC of 0.981 and an F1 score of 0.857 on a test set.
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.