OCTolyzer: Fully automatic toolkit for segmentation and feature extracting in optical coherence tomography and scanning laser ophthalmoscopy data

TL;DR

OCTolyzer is an open-source toolkit that automates segmentation and feature extraction in OCT and SLO eye imaging, enabling rapid, reproducible, and clinically meaningful analysis for ophthalmology research.

Contribution

It introduces the first open-source, deep learning-based toolkit for retinochoroidal analysis in OCT/SLO data, improving standardization and accessibility.

Findings

High reproducibility of choroid thickness measurements (MAE ~6.7μm, correlation 0.99)

Strong reproducibility of macular choroid vascular index (MAE 0.0271, correlation 0.97)

Processing times under three seconds per B-scan on a laptop CPU.

Abstract

Optical coherence tomography (OCT) and scanning laser ophthalmoscopy (SLO) of the eye has become essential to ophthalmology and the emerging field of oculomics, thus requiring a need for transparent, reproducible, and rapid analysis of this data for clinical research and the wider research community. Here, we introduce OCTolyzer, the first open-source toolkit for retinochoroidal analysis in OCT/SLO data. It features two analysis suites for OCT and SLO data, facilitating deep learning-based anatomical segmentation and feature extraction of the cross-sectional retinal and choroidal layers and en face retinal vessels. We describe OCTolyzer and evaluate the reproducibility of its OCT choroid analysis. At the population level, metrics for choroid region thickness were highly reproducible, with a mean absolute error (MAE)/Pearson correlation for macular volume choroid thickness (CT) of 6.7$\mu$m/0.99, macular B-scan CT of 11.6$\mu$m/0.99, and peripapillary CT of 5.0$\mu$m/0.99. Macular choroid vascular index (CVI) also showed strong reproducibility, with MAE/Pearson for volume CVI yielding 0.0271/0.97 and B-scan CVI 0.0130/0.91. At the eye level, measurement noise for regional and vessel metrics was below 5% and 20% of the population's variability, respectively. Outliers were caused by poor-quality B-scans with thick choroids and invisible choroid-sclera boundary. Processing times on a laptop CPU were under three seconds for macular/peripapillary B-scans and 85 seconds for volume scans. OCTolyzer can convert OCT/SLO data into reproducible and clinically meaningful retinochoroidal features and will improve the standardisation of ocular measurements in OCT/SLO image analysis, requiring no specialised training or proprietary software to be used. OCTolyzer is freely available here: https://github.com/jaburke166/OCTolyzer.