Quick start on structured illumination microscopy (SIM)

Option 1: Step-by-step Jupyter notebook

The example notebook can be found in examples/notebook-SIM.ipynb with dense microbead data. After installing the dependencies, you can run the notebook to go over the reconstruction procedure step-by-step.

This notebook is also available to run on Google Colab. Please note that env setup is slightly different on Colab due to the pre-installed dependencies, and you need to follow the instruction in the Colab notebook.

Option 2: Run the python script

  1. Download additional data from this Google Drive and place .npz files in examples folder.

  2. Start running endoplasmic reticulum (ER)-labeled cell reconstruction in commandline. Replace er_cell with mito_cell for mitochondria-labeled cell data.

    $ python nstm/sim3d_main.py --config er_cell

Note

The mito_cell reconstruction takes ~40 minutes (slightly faster for er_cell) on a single NVIDIA A6000 GPU (48GB). er_cell is also runnable on a single NVIDIA RTX 3090 GPU (24GB) when batch_size is set to 1 in the .yaml file. mito_cell requires close to 40GB GPU memory to run, as it has more image planes.

  1. The reconstruction results will be saved in examples/checkpoint/ folder. The 3D reconstruction volume with three timepoints (each corresponding to an illumination orientation) will be saved as recon_filtered.tif, and can be viewed using Fiji. The recovered motion map will be saved as motion_dense_t.npy.

  1. Additional reconstruction parameters are stored in examples/configs/er_cell.yaml and examples/configs/mito_cell.yaml. To print the full parameter descriptions, run:

    $ python nstm/sim3d_main.py --helpfull