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

The nanoscale resolution of super-resolution microscopy has now enabled the
use of fluorescent based molecular localization tools to study whole cell
structural biology. Machine learning based analysis of super-resolution data
offers tremendous potential for discovery of new biology, that by definition is
not known and lacks ground truth. Herein, we describe the application of weakly
supervised learning paradigms to super-resolution microscopy and its potential
to enable the accelerated exploration of the molecular architecture of
subcellular macromolecules and organelles.

本研究介绍了弱监督学习方法在超分辨显微镜技术中的应用，探讨了其加速探索亚细胞大分子和细胞器分子结构的潜力。