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<p>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 often compromise between model performance and its generalization. Researchers have to manually tune simulated training data parameters to resemble 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 super-resolution reconstruction of single-molecule localization microscopy data that are based on Deep-STORM and DeepSTORM3D. Our methods 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 comparable or superior performance of both methods compared to Deep-STORM, DeepSTORM3D, and other state-of-the-art methods, while significantly reducing the manual labor and computation time.</p>

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One-click reconstruction in single-molecule localization microscopy via experimental parameter-aware deep learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月25日

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  • Alon Saguy,
  • Dafei Xiao,
  • Kaarjel K. Narayanasamy,
  • Yuya Nakatani,
  • Nahima Saliba,
  • Gabriella Gagliano,
  • Anna-Karin Gustavsson,
  • Mike Heilemann,
  • Yoav Shechtman

摘要

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 often compromise between model performance and its generalization. Researchers have to manually tune simulated training data parameters to resemble 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 super-resolution reconstruction of single-molecule localization microscopy data that are based on Deep-STORM and DeepSTORM3D. Our methods 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 comparable or superior performance of both methods compared to Deep-STORM, DeepSTORM3D, and other state-of-the-art methods, while significantly reducing the manual labor and computation time.