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

Mask-based integrated fluorescence microscopy is a compact imaging technique for biomedical research. It can perform snapshot 3D imaging through a thin optical mask with a scalable field of view (FOV) and a thin device thickness. Integrated microscopy uses computational algorithms for object reconstruction, but efficient reconstruction algorithms for large-scale data have been lacking. Here, we developed DeepInMiniscope, a miniaturized integrated microscope featuring a custom-designed optical mask and a multi-stage physics-informed deep learning model. This reduces the computational resource demands by orders of magnitude and facilitates fast reconstruction. Our deep learning algorithm can reconstruct object volumes over 4×6×0.6 mm3. We demonstrated substantial improvement in both reconstruction quality and speed compared to traditional methods for large-scale data. Notably, we imaged neuronal activity with near-cellular resolution in awake mouse cortex, representing a substantial leap ..., , , # DeepInMiniscope: Deep-learning-powered physics-informed integrated miniscope

[https://doi.org/10.5061/dryad.6t1g1jx83](https://doi.org/10.5061/dryad.6t1g1jx83)

## Description of the data and file structure

### DeepInMiniscope: Learned Integrated Miniscope

### Datasets, models and codes for 2D and 3D sample reconstructions.

Dataset for 2D reconstruction includes test data for green stained lens tissue.
Input: measured images of green fluorescent stained lens tissue, dissembled into sub-FOV patches.
Output: the slide containing green lens tissue features.

Dataset for 3D sample reconstructions includes test data for 3D reconstruction of in-vivo mouse brain video recording.
Input: Time-series standard-derivation of difference-to-local-mean weighted raw video.
Output: reconstructed 4-D volumetric video containing a 3-dimensional distribution of neural activities.

## Files and variables

### Download data, code, and sample results

1. Download data `data.zip`, code `code.zip`, result...,