Single-Molecule Localization Microscopy (SMLM) has expanded our ability to visualize subcellular structures but is limited in its temporal resolution. Increasing emitter density will improve temporal resolution, but current analysis algorithms struggle as emitter images significantly overlap. Here we present a deep convolutional neural network called LUENN which utilizes a unique architecture that rejects the isolated emitter assumption; it can smoothly accommodate emitters that range from completely isolated to co-located. This architecture, alongside an accurate estimator of location uncertainty, extends the range of usable emitter densities by a factor of 6 to over 31 emitters per micrometer-squared with reduced penalty to localization precision and improved temporal resolution. Apart from providing uncertainty estimation, the algorithm improves usability in laboratories by reducing imaging times and easing requirements for successful experiments.

翻译：单分子定位显微镜（SMLM）拓展了我们对亚细胞结构的可视化能力，但其时间分辨率受到限制。提高发射体密度可改善时间分辨率，然而当前分析算法在处理发射体图像显著重叠时面临困难。本文提出了一种名为LUENN的深度卷积神经网络，该网络采用独特架构摒弃了孤立发射体假设，能够平滑处理从完全孤立到共定位的各类发射体。该架构与精确的位置不确定性估计器相结合，将可用的发射体密度范围扩展了6倍，达到每平方微米超过31个发射体，同时降低了定位精度的损失并提升了时间分辨率。除提供不确定性估计外，该算法还通过缩短成像时间和降低成功实验的门槛，提升了实验室的实用性。