SLOctolyzer: Fully automatic analysis toolkit for segmentation and feature extracting in scanning laser ophthalmoscopy images

TL;DR

SLOctolyzer is the first open-source tool to convert raw SLO images into reproducible and clinically meaningful retinal vascular parameters, and requires no specialist knowledge or proprietary software, and allows manual correction of segmentations and re-computing of vascular metrics.

Abstract

Purpose: The purpose of this study was to introduce SLOctolyzer: an open-source analysis toolkit for en face retinal vessels in infrared reflectance scanning laser ophthalmoscopy (SLO) images. Methods: SLOctolyzer includes two main modules: segmentation and measurement. The segmentation module uses deep learning methods to delineate retinal anatomy, and detects the fovea and optic disc, whereas the measurement module quantifies the complexity, density, tortuosity, and calibre of the segmented retinal vessels. We evaluated the segmentation module using unseen data and measured its reproducibility. Results: SLOctolyzer's segmentation module performed well against unseen internal test data (Dice for all-vessels = 0.91; arteries = 0.84; veins = 0.85; optic disc = 0.94; and fovea = 0.88). External validation against severe retinal pathology showed decreased performance (Dice for arteries = 0.72; veins = 0.75; and optic disc = 0.90). SLOctolyzer had good reproducibility (mean difference for fractal dimension = -0.001; density = -0.0003; calibre = -0.32 microns; and tortuosity density = 0.001). SLOctolyzer can process a 768 x 768 pixel macula-centred SLO image in under 20 seconds and a disc-centred SLO image in under 30 seconds using a 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 SLOctolyzer 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.

Figures (19)

• Figure 1: Summary of SLOctolyzer's analysis pipeline with a segmentation module (a) for binary vessel/artery/vein segmentation and fovea/optic disc detection, and a measurement module (b) for measuring retinal vascular parameters, with additional functionality for metadata extraction and manual annotation.
• Figure 2: (A) Disc-centred SLO image with segmentations and regions of interest overlaid. (B) Region of interest masks for the whole image (green), zone C (blue) and zone B (red) with the names of vessel metric, colour coordinated with arrows indicating which regions they are measured in. (C) Flowchart of which vessel metrics are computed for which segmentation map.
• Figure 3: Predicted segmentation masks from each model for two examples randomly selected from their internal test sets.
• Figure 4: Visual comparison between ground truth and predicted segmentations for the external test set, RAVIR, for artery-vein-optic disc detection. The images were selected to show progressing eye pathology from left to right.
• Figure 5: Bland-Altman plots of residual distributions from assessing the reproducibility of SLOctolyzer's segmentation module across all retinal vessel metrics. Scatter-plot residuals for arteries (red), veins (blue) and all-vessel (orange) are overlaid together.