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DeepInMiniscope: Deep learning-powered physics-informed integrated miniscope.

Feng Tian1, Ben Mattison2,3, Weijian Yang1,3

  • 1Department of Electrical and Computer Engineering, University of California, Davis, Davis, CA 95616, USA.

Science Advances
|September 12, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

DeepInMiniscope, a novel integrated microscope, uses a physics-informed deep learning model for fast, high-quality 3D reconstruction of large-scale biological data. This compact device enables scalable, large field-of-view imaging for biomedical research.

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Area of Science:

  • Biomedical imaging
  • Microscopy
  • Deep learning applications

Background:

  • Mask-based integrated fluorescence microscopy offers compact, snapshot 3D imaging with scalable fields of view (FOV).
  • Existing computational reconstruction algorithms struggle with efficiency for large-scale data.
  • A need exists for improved reconstruction speed and quality in integrated microscopy.

Purpose of the Study:

  • To develop DeepInMiniscope, a miniaturized integrated microscope with a custom optical mask.
  • To create an efficient physics-informed deep learning model for rapid 3D object reconstruction.
  • To demonstrate enhanced reconstruction quality and speed for large-scale imaging data.

Main Methods:

  • Development of a miniaturized integrated microscope with a novel optical mask.
  • Implementation of a physics-informed deep learning algorithm for 3D object reconstruction.
  • Reconstruction of large volumetric data (4mm x 6mm x 0.6mm).

    • Main Results:

    • DeepInMiniscope achieved significant improvements in reconstruction quality and speed over traditional methods.
    • The deep learning model successfully reconstructed large object volumes.
    • Near-cellular resolution imaging of neuronal activity in awake mouse cortex was demonstrated.

    Conclusions:

    • DeepInMiniscope represents a substantial advancement over existing integrated microscopes.
    • The system offers scalable, large-FOV, high-speed 3D imaging capabilities.
    • This technology holds promise for diverse biomedical research applications requiring compact imaging devices.