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. ### Competing Interest Statement The authors have declared no competing interest.
原文链接
分享
引用
