  Purpose: To describe SLOctolyzer: an open-source analysis toolkit for en face
retinal vessels appearing in infrared reflectance scanning laser ophthalmoscopy
(SLO) images.
  Methods: SLOctolyzer includes two main modules: segmentation and measurement.
The segmentation module use deep learning methods to delineate retinal anatomy,
while the measurement module quantifies key retinal vascular features such as
vessel complexity, density, tortuosity, and calibre. We evaluate the
segmentation module using unseen data and measure its reproducibility.
  Results: SLOctolyzer's segmentation module performed well against unseen
internal test data (Dice for all-vessels, 0.9097; arteries, 0.8376; veins,
0.8525; optic disc, 0.9430; fovea, 0.8837). External validation against severe
retinal pathology showed decreased performance (Dice for arteries, 0.7180;
veins, 0.7470; optic disc, 0.9032). SLOctolyzer had good reproducibility (mean
difference for fractal dimension, -0.0007; vessel density, -0.0003; vessel
calibre, -0.3154 $\mu$m; tortuosity density, 0.0013). SLOctolyzer can process a
macula-centred SLO image in under 20 seconds and a disc-centred SLO image in
under 30 seconds using a standard laptop CPU.
  Conclusions: To our knowledge, SLOctolyzer is the first open-source tool to
convert raw SLO images into reproducible and clinically meaningful retinal
vascular parameters. SLO images are captured simultaneous to optical coherence
tomography (OCT), and we believe our software will be useful for extracting
retinal vascular measurements from large OCT image sets and linking them to
ocular or systemic diseases. It requires no specialist knowledge or proprietary
software, and allows manual correction of segmentations and re-computing of
vascular metrics. SLOctolyzer is freely available at
https://github.com/jaburke166/SLOctolyzer.
