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Volumetric localization microscopy with deep learning
Keyi Han1,2, Xuanwen Hua1,2, Tianrui Qi1,3
1Laboratory for Systems Biophotonics, Georgia Institute of Technology, Atlanta, GA, USA.
Nature Communications
|December 8, 2025
Summary
Volumetric localization microscopy (VLM) with deep learning achieves high-fidelity 3D single-molecule imaging. This super-resolution technique offers hardware simplicity and data efficiency for advanced biomedical research.
Area of Science:
- Biophysics
- Optical Microscopy
- Computational Biology
Background:
- Super-resolution microscopy, especially localization-based methods, faces challenges in balancing optical complexity, computational load, and user accessibility.
- Current approaches often use deterministic or learning-based methods separately, missing synergistic potential.
Purpose of the Study:
- To introduce volumetric localization microscopy (VLM) with deep learning for high-fidelity 3D single-molecule imaging.
- To integrate instrumental and algorithmic innovations for improved super-resolution microscopy.
Main Methods:
- VLM utilizes a wavefront-optimized light-field configuration for single-molecule data acquisition.
- A cascaded neural network reconstructs 3D volumes and determines molecular coordinates.
- The system is trained using system-aware intrinsic point-spread functions, eliminating the need for external modalities or sample-specific data.
Main Results:
- Achieved 10 nm lateral and 25 nm axial localization precision.
- Demonstrated effective imaging depth over 4 µm.
- Validated across diverse biological specimens, showing hardware simplicity, data efficiency, and minimal phototoxicity.
Conclusions:
- VLM with deep learning offers a powerful, accessible super-resolution technique for 3D single-molecule imaging.
- This method overcomes limitations of current fluorescence microscopy, paving the way for broader biomedical research advancements.
