SLOctolyzer: Fully Automatic Analysis Toolkit for Segmentation and Feature Extracting in Scanning Laser Ophthalmoscopy Images.

IF 2.6 3区医学 Q2 OPHTHALMOLOGY Translational Vision Science & Technology Pub Date : 2024-11-04 DOI:10.1167/tvst.13.11.7

Jamie Burke, Samuel Gibbon, Justin Engelmann, Adam Threlfall, Ylenia Giarratano, Charlene Hamid, Stuart King, Ian J C MacCormick, Thomas J MacGillivray

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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 caliber 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; caliber = -0.32 microns; and tortuosity density = 0.001). SLOctolyzer can process a 768 × 768 pixel macula-centered SLO image in under 20 seconds and a disc-centered 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. 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.

Translational relevance: 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.

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SLOctolyzer：用于扫描激光眼底镜图像分割和特征提取的全自动分析工具包。

目的：本研究旨在介绍 SLOctolyzer：一种开源分析工具包，用于红外反射扫描激光眼底镜（SLO）图像中的视网膜血管：SLOctolyzer 包括两个主要模块：分割和测量。分割模块使用深度学习方法划分视网膜解剖结构，并检测眼窝和视盘，而测量模块则量化分割视网膜血管的复杂度、密度、迂曲度和口径。我们使用未见数据对分割模块进行了评估，并测量了其可重复性：结果：SLOctolyzer 的分割模块在未见内部测试数据时表现良好（所有血管的 Dice = 0.91；动脉 = 0.84；静脉 = 0.85；视盘 = 0.94；眼窝 = 0.88）。针对严重视网膜病变的外部验证显示性能下降（动脉骰子=0.72；静脉骰子=0.75；视盘骰子=0.90）。SLOctolyzer 的重现性良好（分形维度的平均差 = -0.001；密度 = -0.0003；口径 = -0.32微米；迂曲密度 = 0.001）。SLOctolyzer 可在 20 秒内处理以黄斑为中心的 768 × 768 像素 SLO 图像，使用笔记本电脑 CPU 可在 30 秒内处理以圆盘为中心的 SLO 图像：据我们所知，SLOctolyzer 是首个将原始 SLO 图像转换为可重复且具有临床意义的视网膜血管参数的开源工具。它不需要专业知识或专有软件，可手动校正分割和重新计算血管参数。SLOctolyzer 可通过 https://github.com/jaburke166/SLOctolyzer.Translational 免费获取：SLO 图像与光学相干断层扫描 (OCT) 图像同时采集，我们相信 SLOctolyzer 将有助于从大型 OCT 图像集中提取视网膜血管测量值，并将其与眼部或全身疾病联系起来。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Translational Vision Science & Technology Engineering-Biomedical Engineering

CiteScore

5.70

自引率

3.30%

发文量

346

审稿时长

25 weeks

期刊介绍： Translational Vision Science & Technology (TVST), an official journal of the Association for Research in Vision and Ophthalmology (ARVO), an international organization whose purpose is to advance research worldwide into understanding the visual system and preventing, treating and curing its disorders, is an online, open access, peer-reviewed journal emphasizing multidisciplinary research that bridges the gap between basic research and clinical care. A highly qualified and diverse group of Associate Editors and Editorial Board Members is led by Editor-in-Chief Marco Zarbin, MD, PhD, FARVO. The journal covers a broad spectrum of work, including but not limited to: Applications of stem cell technology for regenerative medicine, Development of new animal models of human diseases, Tissue bioengineering, Chemical engineering to improve virus-based gene delivery, Nanotechnology for drug delivery, Design and synthesis of artificial extracellular matrices, Development of a true microsurgical operating environment, Refining data analysis algorithms to improve in vivo imaging technology, Results of Phase 1 clinical trials, Reverse translational ("bedside to bench") research. TVST seeks manuscripts from scientists and clinicians with diverse backgrounds ranging from basic chemistry to ophthalmic surgery that will advance or change the way we understand and/or treat vision-threatening diseases. TVST encourages the use of color, multimedia, hyperlinks, program code and other digital enhancements.