返回
Event-driven acquisition for content-enriched microscopy
DOI:10.1038/s41592-022-01589-x.png)
摘要
En 中文
Event-driven acquisition uses neural-network-based recognition of specific biological events to trigger switching between slow and fast super-resolution imaging, enriching the capture of interesting events with high spatiotemporal resolution. A common goal of fluorescence microscopy is to collect data on specific biological events. Yet, the event-specific content that can be collected from a sample is limited, especially for rare or stochastic processes. This is due in part to photobleaching and phototoxicity, which constrain imaging speed and duration. We developed an event-driven acquisition framework, in which neural-network-based recognition of specific biological events triggers real-time control in an instant structured illumination microscope. Our setup adapts acquisitions on-the-fly by switching between a slow imaging rate while detecting the onset of events, and a fast imaging rate during their progression. Thus, we capture mitochondrial and bacterial divisions at imaging rates that match their dynamic timescales, while extending overall imaging durations. Because event-driven acquisition allows the microscope to respond specifically to complex biological events, it acquires data enriched in relevant content.
Keyword:
LIVE CELLS
PHOTOTOXICITY
FLUORESCENT
MORPHOLOGY
DRP1
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
32.1
论文数:
7.2K
被引数:
12.7W
机构
引用论文
A machine learning approach for online automated optimization of super-resolution optical microscopy
NATURE COMMUNICATIONS
IF15.7
Adaptive light-sheet microscopy for long-term, high-resolution imaging in living organisms
NATURE BIOTECHNOLOGY
IF41.7
Deep learning massively accelerates super-resolution localization microscopy深度学习大规模加速超分辨率定位显微镜
NATURE BIOTECHNOLOGY
IF41.7

