# A publicly available vessel segmentation algorithm for SLO images

arXiv: https://arxiv.org/abs/2311.17525 · HTML: https://mlsanity.com/paper/2311.17525/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 29 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation](https://mlsanity.com/paper/1802.06955/) (2018)
- [DUNet: A deformable network for retinal vessel segmentation](https://mlsanity.com/paper/1811.01206/) (2018)
- [IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks](https://mlsanity.com/paper/1912.05763/) (2019)
- [RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging](https://mlsanity.com/paper/2203.14928/) (2022)

## Built on by (most cited first, 1 of 1)

- [SLOctolyzer: Fully automatic analysis toolkit for segmentation and feature extracting in scanning laser ophthalmoscopy images](https://mlsanity.com/paper/2406.16466/) (2024)

## Abstract (arXiv)

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.