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<p>Super-resolution (SR) neural networks transform low-resolution optical microscopy images into SR images. Application of single-image SR (SISR) methods to long-term imaging has not exploited the temporal dependencies between neighboring frames and has been subject to inference uncertainty that is difficult to quantify. Here, by building a large-scale fluorescence microscopy dataset and evaluating the propagation and alignment components of neural network models, we devise a deformable phase-space alignment (DPA) time-lapse image SR (TISR) neural network. DPA-TISR adaptively enhances the cross-frame alignment in the phase domain and outperforms existing state-of-the-art SISR and TISR models. We also develop Bayesian DPA-TISR and design an expected calibration error minimization framework that reliably infers inference confidence. We demonstrate multicolor live-cell SR imaging for more than 10,000 time points of various biological specimens with high fidelity, temporal consistency and accurate confidence quantification.</p>

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A neural network for long-term super-resolution imaging of live cells with reliable confidence quantification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月29日

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  • Chang Qiao,
  • Shuran Liu,
  • Yuwang Wang,
  • Wencong Xu,
  • Xiaohan Geng,
  • Tao Jiang,
  • Jingyu Zhang,
  • Quan Meng,
  • Hui Qiao,
  • Dong Li,
  • Qionghai Dai

摘要

Super-resolution (SR) neural networks transform low-resolution optical microscopy images into SR images. Application of single-image SR (SISR) methods to long-term imaging has not exploited the temporal dependencies between neighboring frames and has been subject to inference uncertainty that is difficult to quantify. Here, by building a large-scale fluorescence microscopy dataset and evaluating the propagation and alignment components of neural network models, we devise a deformable phase-space alignment (DPA) time-lapse image SR (TISR) neural network. DPA-TISR adaptively enhances the cross-frame alignment in the phase domain and outperforms existing state-of-the-art SISR and TISR models. We also develop Bayesian DPA-TISR and design an expected calibration error minimization framework that reliably infers inference confidence. We demonstrate multicolor live-cell SR imaging for more than 10,000 time points of various biological specimens with high fidelity, temporal consistency and accurate confidence quantification.