Saguy Alon, Xiao Dafei, Narayanasamy Kaarjel K, Nakatani Yuya, Gustavsson Anna-Karin, Heilemann Mike, Shechtman Yoav
Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel.
Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, Israel.
bioRxiv. 2025 Apr 18:2025.04.13.648574. doi: 10.1101/2025.04.13.648574.
Deep neural networks have led to significant advancements in microscopy image generation and analysis. In single-molecule localization based super-resolution microscopy, neural networks are capable of predicting fluorophore positions from high-density emitter data, thus reducing acquisition time, and increasing imaging throughput. However, neural network-based solutions in localization microscopy require intensive human intervention and computation expertise to address the compromise between model performance and its generalization. For example, researchers manually tune parameters to generate training images that are similar to their experimental data; thus, for every change in the experimental conditions, a new training set should be manually tuned, and a new model should be trained. Here, we introduce AutoDS and AutoDS3D, two software programs for reconstruction of single-molecule super-resolution microscopy data that are based on Deep-STORM and DeepSTORM3D, that significantly reduce human intervention from the analysis process by automatically extracting the experimental parameters from the imaging raw data. In the 2D case, AutoDS selects the optimal model for the analysis out of a set of pre-trained models, hence, completely removing user supervision from the process. In the 3D case, we improve the computation efficiency of DeepSTORM3D and integrate the lengthy workflow into a graphic user interface that enables image reconstruction with a single click. Ultimately, we demonstrate superior performance of both pipelines compared to Deep-STORM and DeepSTORM3D for single-molecule imaging data of complex biological samples, while significantly reducing the manual labor and computation time.
深度神经网络已在显微镜图像生成和分析方面取得了重大进展。在基于单分子定位的超分辨率显微镜中，神经网络能够从高密度发射体数据预测荧光团位置，从而减少采集时间并提高成像通量。然而，定位显微镜中基于神经网络的解决方案需要大量人工干预和计算专业知识来解决模型性能与其泛化能力之间的权衡问题。例如，研究人员手动调整参数以生成与实验数据相似的训练图像；因此，对于实验条件的每一次变化，都需要手动调整新的训练集并训练新模型。在此，我们介绍AutoDS和AutoDS3D，这两个基于Deep-STORM和DeepSTORM3D的用于单分子超分辨率显微镜数据重建的软件程序，它们通过从成像原始数据中自动提取实验参数，显著减少了分析过程中的人工干预。在二维情况下，AutoDS从一组预训练模型中选择用于分析的最优模型，从而完全消除了该过程中的用户监督。在三维情况下，我们提高了DeepSTORM3D的计算效率，并将冗长的工作流程集成到一个图形用户界面中，实现一键式图像重建。最终，我们证明了与Deep-STORM和DeepSTORM3D相比，这两种流程在复杂生物样品的单分子成像数据方面具有卓越性能，同时显著减少了人工劳动和计算时间。
