OCTolyzer: Fully automatic analysis toolkit for segmentation and feature extracting in optical coherence tomography (OCT) and scanning laser ophthalmoscopy (SLO) data

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.

Evaluation of an automated choroid segmentation algorithm in a longitudinal kidney donor and recipient cohort

This study evaluated the performance of an automated choroid segmentation algorithm in enhanced depth imaging optical coherence tomography (EDI-OCT) images from a longitudinal kidney donor and recipient cohort. We assessed 22 donors and 23 patients with end-stage kidney disease during the course of donating and receiving a kidney transplant, respectively, over a period of 1 year. We assessed choroid thickness and area on EDI-OCT scans and compared our automated measurements to manual ones at the same locations. We estimated associations between measurements of the choroid and markers of renal function (serum urea and creatinine, estimated glomerular filtration rate (eGFR)) using correlation and linear mixed-effects models. There was good agreement between manual and automated measures. Automated measures were more precise because of smaller measurement error, especially with repeated measures over time. Associations with renal function were stronger with automated measures (creatinine P=0.01, eGFR P=0.02) compared to manual ones (creatinine P=0.12, eGFR P=0.06). Significant linear associations were observed between the choroid and urea, creatinine, and eGFR in recipients, and urea in donors. Our automated approach has greater precision than manual measurements. Greater longitudinal reproducibility of automated measurements may explain stronger associations with renal function compared to manual measurements. To improve detection of meaningful associations with clinical endpoints in longitudinal studies of OCT, reducing measurement error should be a priority, and automated measurements help achieve this.