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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 over existing integrated microscopes. DeepInMiniscope holds great promise for scalable, large-FOV, high-speed, 3D imaging applications with compact device footprint. # 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`, results `results.zip`. 2. Unzip the downloaded files and place them in the same main folder. 3. Confirm that the main folder contains three subfolders: `data`, `code`, and `results`. Inside the `data` and `code` folder, there should be subfolders for each test case. ## Data 2D_lenstissue **data_2d_lenstissue.mat:** Measured images of green fluorescent stained lens tissue, disassembled into sub-FOV patches. * **Xt:** stacked 108 FOVs of measured image, each centered at one microlens unit with 720 x 720 pixels. Data dimension in order of (batch, height, width, FOV). * **Yt:** placeholder variable for reconstructed object, each centered at corresponding microlens unit, with 180 x 180 voxels. Data dimension in order of (batch, height, width, FOV). **reconM_0308:** Trained Multi-FOV ADMM-Net model for 2D lens tissue reconstruction. **gen_lenstissue.mat:** Generated lens tissue reconstruction by running the model with code **2D_lenstissue.py** * **generated_images:** stacked 108 reconstructed FOVs of lens tissue sample by multi-FOV ADMM-Net, the assembled full sample reconstruction is shown in results/2D_lenstissue_reconstruction.png 3D_mouse **reconM_g704_z5_v4:** Trained 3D Multi-FOV ADMM-Net model for 3D sample reconstructions **t_img_recd_video0003 24-04-04 18-31-11_abetterrecordlong_03560_1_290.mat:** Time-series standard-deviation of difference-to-local-mean weighted raw video. * **Xts:** test video with 290 frames and each frame 6 FOVs, with 1408 x 1408 pixels per FOV. Data dimension in order of (frames, height, width, FOV). **gen_img_recd_video0003 24-04-04 18-31-11_abetterrecordlong_03560_1_290_v4.mat:** Generated 4D volumetric video containing 3-dimensional distribution of neural activities. * **generated_images_fu:** frame-by-frame 3D reconstruction of recorded video in uint8 format. Data dimension in order of (batch, FOV, height, width, depth). Each frame contains 6 FOVs, and each FOV has 13 reconstruction depths with 416 x 416 voxels per depth. Variables inside saved model subfolders (reconM_0308 and reconM_g704_z5_v4): * **saved_model.pb:** model computation graph including architecture and input/output definitions. * **keras_metadata.pb:** Keras metadata for the saved model, including model class, training configuration, and custom objects. * **assets:** external files for custom assets loaded during model training/inference. This folder is empty, as the model does not use custom assets. * **variables.data-00000-of-00001:** numerical values of model weights and parameters. * **variables.index:** index file that maps variable names to weight locations in .data. ## Code/software ### Set up the Python environment 1. Download and install the [Anaconda distribution](https://www.anaconda.com/download). 2. The code was tested with the following packages * python=3.9.7 * tensorflow=2.7.0 * keras=2.7.0 * matplotlib=3.4.3 * scipy=1.7.1 ## Code **2D_lenstissue.py:** Python code for Multi-FOV ADMM-Net model to generate reconstruction results. The function of each script section is described at the beginning of each section. **lenstissue_2D.m:** Matlab code to display the generated image and reassemble sub-FOV patches. **sup_psf.m:** Matlab script to load microlens coordinates data and to generate PSF pattern. **lenscoordinates.xls:** Microlens units coordinates table. **3D mouse.py:** Python code for Multi-FOV ADMM-Net model to generate reconstruction results. The function of each script section is described at the beginning of each section. **mouse_3D.m:** Matlab code to display the reconstructed neural activity video and to calculate temporal correlation. ## Access information Other publicly accessible locations of the data: * [https://github.com/Yang-Research-Laboratory/DeepInMiniscope-Learned-Integrated-Miniscope](https://github.com/Yang-Research-Laboratory/DeepInMiniscope-Learned-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). 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 an efficient physics-informed deep learning model that markedly reduces computational demand. Parts of the 3D object can be individually reconstructed and combined. Our deep learning algorithm can reconstruct object volumes over 4 millimeters by 6 millimeters by 0.6 millimeters. 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 over existing integrated microscopes. DeepInMiniscope holds great promise for scalable, large-FOV, high-speed, 3D imaging applications with compact device footprint. 

Single-shot 3D optical microscopy that can capture high-resolution information over a large volume has broad applications in biology. Existing 3D imaging methods using point-spread-function (PSF) engineering often have limited depth of field (DOF) or require custom and often complex design of phase masks. We propose a new, to the best of our knowledge, PSF approach that is easy to implement and offers a large DOF. The PSF appears to be axially V-shaped, engineered by replacing the conventional tube lens with a pair of axicon lenses behind the objective lens of a wide-field microscope. The 3D information can be reconstructed from a single-shot image using a deep neural network. Simulations in a 10× magnification wide-field microscope show the V-shaped PSF offers excellent 3D resolution (<2.5 µm lateral and ∼15 µm axial) over a ∼350 µm DOF at a 550 nm wavelength. Compared to other popular PSFs designed for 3D imaging, the V-shaped PSF is simple to deploy and provides high 3D reconstruction quality over an extended DOF.