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.

这篇研究论文介绍了一种新型的深度卷积神经网络 LUENN，可以有效提高单分子定位显微镜技术的亚细胞结构可视化的时间分辨率，通过准确的位置不确定性估计器，成功应用于密度高达 31 个 / 平方微米的多个发射器的图像定位。