Single-molecule localization microscopy (SMLM) has had remarkable success in imaging cellular structures with nanometer resolution, but the need for activating only single isolated emitters limits imaging speed and labeling density. Here, we overcome this major limitation using deep learning. We developed DECODE, a computational tool that can localize single emitters at high density in 3D with highest accuracy for a large range of imaging modalities and conditions. In a public software benchmark competition, it outperformed all other fitters on 12 out of 12 data-sets when comparing both detection accuracy and localization error, often by a substantial margin. DECODE allowed us to take live-cell SMLM data with reduced light exposure in just 3 seconds and to image microtubules at ultra-high labeling density. Packaged for simple installation and use, DECODE will enable many labs to reduce imaging times and increase localization density in SMLM.

中文翻译：

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深度学习可实现高精度的快速密集分子单分子定位

单分子定位显微镜（SMLM）在以纳米分辨率成像细胞结构方面取得了显著成功，但是仅激活单个孤立发射器的需求限制了成像速度和标记密度。在这里，我们使用深度学习克服了这一主要限制。我们开发了DECODE，这是一种计算工具，可以在各种成像模式和条件下以最高的精度以3D方式以高密度定位单个发射器。在一个公共软件基准测试竞赛中，在比较检测精度和定位误差时，它在12个数据集中的12个数据集上的表现优于所有其他装配工，通常要大幅度提高。DECODE使我们能够在短短3秒钟内以减少的曝光量获取活细胞SMLM数据，并以超高标记密度对微管成像。打包成易于安装和使用的方式，