  Purpose: To describe OCTolyzer: an open-source toolkit for retinochoroidal
analysis in optical coherence tomography (OCT) and scanning laser
ophthalmoscopy (SLO) images.
  Method: OCTolyzer has two analysis suites, for SLO and OCT images. The former
enables anatomical segmentation and feature measurement of the en face retinal
vessels. The latter leverages image metadata for retinal layer segmentations
and deep learning-based choroid layer segmentation to compute retinochoroidal
measurements such as thickness and volume. We introduce OCTolyzer and assess
the reproducibility of its OCT analysis suite for choroid analysis.
  Results: At the population-level, choroid region metrics were highly
reproducible (Mean absolute error/Pearson/Spearman correlation for macular
volume choroid thickness (CT):6.7$\mu$m/0.9933/0.9969, macular B-scan
CT:11.6$\mu$m/0.9858/0.9889, peripapillary CT:5.0$\mu$m/0.9942/0.9940). Macular
choroid vascular index (CVI) had good reproducibility (volume
CVI:0.0271/0.9669/0.9655, B-scan CVI:0.0130/0.9090/0.9145). At the eye-level,
measurement error in regional and vessel metrics were below 5% and 20% of the
population's variability, respectively. Major outliers were from poor quality
B-scans with thick choroids and invisible choroid-sclera boundary.
  Conclusions: OCTolyzer is the first open-source pipeline to convert OCT/SLO
data into reproducible and clinically meaningful retinochoroidal measurements.
OCT processing on a standard laptop CPU takes under 2 seconds for macular or
peripapillary B-scans and 85 seconds for volume scans. OCTolyzer can help
improve standardisation in the field of OCT/SLO image analysis and is freely
available here: https://github.com/jaburke166/OCTolyzer.
