Single-molecule localization microscopy techniques, like stochastic optical
reconstruction microscopy (STORM), visualize biological specimens by
stochastically exciting sparse blinking emitters. The raw images suffer from
unwanted background fluorescence, which must be removed to achieve
super-resolution. We introduce a sparsity-based background removal method by
adapting a neural network (SLNet) from a different microscopy domain. The SLNet
computes a low-rank representation of the images, and then, by subtracting it
from the raw images, the sparse component is computed, representing the frames
without the background. We compared our approach with widely used background
removal methods, such as the median background removal or the rolling ball
algorithm, on two commonly used STORM datasets, one glial cell, and one
microtubule dataset. The SLNet delivers STORM frames with less background,
leading to higher emitters' localization precision and higher-resolution
reconstructed images than commonly used methods. Notably, the SLNet is
lightweight and easily trainable (<5 min). Since it is trained in an
unsupervised manner, no prior information is required and can be applied to any
STORM dataset. We uploaded a pre-trained SLNet to the Bioimage model zoo,
easily accessible through ImageJ. Our results show that our sparse
decomposition method could be an essential and efficient STORM pre-processing
tool.

一种基于稀疏性的背景去除方法通过使用来自不同显微镜领域的神经网络（SLNet）计算图像的低秩表示，并从原始图像中减去它，从而计算出不带背景的稀疏成分，该方法在比较常用的背景去除方法上实现了更少背景的 STORM 帧，提高了荧光发射体的定位精度和高分辨率图像的重构。

基于稀疏性的 STORM 超分辨率图像背景去除

Sparsity-based background removal for STORM super-resolution images

This study introduces a structural-based training approach for CNN-based
algorithms in single-molecule localization microscopy (SMLM) and 3D object
reconstruction. We compare this approach with the traditional random-based
training method, utilizing the LUENN package as our AI pipeline. The
quantitative evaluation demonstrates significant improvements in detection rate
and localization precision with the structural-based training approach,
particularly in varying signal-to-noise ratios (SNRs). Moreover, the method
effectively removes checkerboard artifacts, ensuring more accurate 3D
reconstructions. Our findings highlight the potential of the structural-based
training approach to advance super-resolution microscopy and deepen our
understanding of complex biological systems at the nanoscale.

本研究介绍了一种基于结构的训练方法，用于单分子定位显微镜（SMLM）和三维物体重建的基于卷积神经网络（CNN）的算法。通过与传统的基于随机的训练方法相比较，利用 LUENN 软件包作为我们的人工智能管道，定量评估表明结构化训练方法在检测率和定位精度上有显著的提升，特别是在信噪比（SNR）变化的情况下。此外，该方法有效地消除了棋盘格伪影，确保更准确的三维重建。我们的研究结果突显了结构化训练方法在推进超分辨显微镜技术并深入理解纳米尺度下复杂生物系统方面的潜力。

基于结构的训练对三维定位精度和质量的影响

Effect of structure-based training on 3D localization precision and  quality

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。