EVE 是一款开放式模块化数据分析软件,用于基于事件的定位显微镜分析

Laura M Weber, Koen J.A. Martens, Clément Cabriel, Joel J. Gates, Manon Albecq, Fredrik Vermeulen, Katharina Hein, Ignacio Izeddin, Ulrike Endesfelder
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摘要

基于事件的传感器(EBS),或称神经形态视觉传感器,提供了一种新颖的成像方法,它可以异步记录光强变化,而不像传统相机那样在固定的曝光时间内捕捉光线。这种功能可实现高时间分辨率、减少数据冗余和宽动态范围。这使得 EBS 成为单分子定位显微镜(SMLM)的理想选择,因为 SMLM 依靠对稀疏、闪烁的荧光发射体进行连续成像来实现超分辨率。最近的研究表明,EBS 能有效捕捉这些发射体,实现与传统相机相当的空间分辨率。然而,现有的基于事件的 SMLM(eveSMLM)数据分析都依赖于将事件列表转换成图像帧进行传统分析,从而限制了该技术潜力的充分发挥。为了克服这一局限,我们开发了用于分析 eveSMLM 数据的专用软件 EVE。EVE 为检测、定位和后处理提供了一个集成平台,并针对 eveSMLM 数据的独特结构提供了各种算法选项。EVE 用户界面友好,采用开放式模块化基础架构,支持持续开发和优化。EVE 是首个基于事件的 SMLM 专用工具,它改变了分析流程,充分利用了 EBS 生成的时空数据。这使得研究人员能够充分挖掘 eveSMLM 的潜力,并鼓励开发新的分析方法和实验改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EVE is an open modular data analysis software for event-based localization microscopy
Event-based sensors (EBS), or neuromorphic vision sensors, offer a novel approach to imaging by recording light intensity changes asynchronously, unlike conventional cameras that capture light over fixed exposure times. This capability results in high temporal resolution, reduced data redundancy, and a wide dynamic range. This makes EBS ideal for Single-Molecule Localization Microscopy (SMLM) as SMLM relies on the sequential imaging of sparse, blinking fluorescent emitters to achieve super-resolution. Recent studies have shown that EBS can effectively capture these emitters, achieving spatial resolution comparable to traditional cameras. However, existing analyses of event-based SMLM (eveSMLM) data have relied on converting event lists into image frames for conventional analysis, limiting the full potential of the technology. To overcome this limitation, we developed EVE, a specialized software for analyzing eveSMLM data. EVE offers an integrated platform for detection, localization, and post-processing, with various algorithmic options tailored for the unique structure of eveSMLM data. EVE is user-friendly and features an open, modular infrastructure that supports ongoing development and optimization. EVE is the first dedicated tool for event-based SMLM, transforming the analysis process to fully utilize the spatiotemporal data generated by EBS. This allows researchers to explore the full potential of eveSMLM and encourages the development of new analytical methods and experimental improvements.
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