OCTolyzer: Fully automatic analysis toolkit for segmentation and feature extracting in optical coherence tomography (OCT) and scanning laser ophthalmoscopy (SLO) data
<|reference_start|>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.<|reference_end|>
@article{burke2024octolyzer:,
    title={OCTolyzer: Fully automatic analysis toolkit for segmentation and feature
  extracting in optical coherence tomography (OCT) and scanning laser
  ophthalmoscopy (SLO) data},
    author={Jamie Burke, Justin Engelmann, Samuel Gibbon, Charlene Hamid, Diana
  Moukaddem, Dan Pugh, Tariq Farrah, Niall Strang, Neeraj Dhaun, Tom
  MacGillivray, Stuart King and Ian J.C. MacCormick},
    journal={arXiv preprint arXiv:2407.14128},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14128},
    primaryClass={eess.IV cs.CV}
}