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

• URL: http://arxiv.org/abs/2406.16466v1
• Date: Mon, 24 Jun 2024 09:16:17 GMT
• Title: SLOctolyzer: Fully automatic analysis toolkit for segmentation and feature extracting in scanning laser ophthalmoscopy images
• Authors: Jamie Burke, Samuel Gibbon, Justin Engelmann, Adam Threlfall, Ylenia Giarratano, Charlene Hamid, Stuart King, Ian J. C. MacCormick, Tom MacGillivray,
• Abstract summary: open-source analysis toolkit for en face retinal vessels appearing in infrared reflectance scanning laser ophthalmoscopy (SLO) images. The segmentation module use deep learning methods to delineate retinal anatomy. The measurement module quantifies key retinal vascular features such as vessel complexity, density, tortuosity, and calibre.
• Score: 4.205028392035434
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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