Flamingo Tools

Data processing for light-sheet microscopy, specifically for data from Flamingo microscopes.

The flamingo_tools library implements functionality for:

• converting the lightsheet data into a format compatible with BigDataViewer and BigStitcher.
• Cell / nucleus segmentation via a 3D U-net.
• ... and more functionality is planned!

This is work in progress!

Requirements & Installation

You need a python environment with the following dependencies: pybdv and z5py. You install these dependencies with mamba or conda via:

conda install -c conda-forge z5py pybdv cluster_tools

(for an existing conda environment). You can also set up a new environment with all required dependencies using the file environment.yaml:

conda env create -f environment.yaml

This will create the environment flamingo, which you can then activate via conda activate flamingo. Finally, to install flamingo_tools into the environment run

pip install -e .

Usage

We provide a command line tool, convert_flamingo, for converting data from the flamingo microscope to a data format compatible with BigDataViewer / BigStitcher:

convert_flamingo -i /path/to/data -o /path/to/output.n5 --file_ext .tif

Here, /path/to/data is the filepath to the folder with the flamingo data to be converted, /path/to/output.n5 is the filepath where the converted data will be stored, and --file_ext .tif declares that the files are stored as tif stacks. Use --file_ext .raw isntead if the data is stored in raw files.

The data will be converted to the bdv.n5 format. It can be opened with BigDataViewer via Plugins->BigDataViewer->Open XML/HDF5. Or with BigStitcher as described here.

You can also check out the following example scripts:

• create_synthetic_data.py: create small synthetic test data to check that the scripts work.
• convert_flamingo_data_examples.py: convert flamingo data to a file format comatible with BigDataViewer / BigStitcher with parameters defined in the python script. Contains two example functions:
  • convert_synthetic_data to convert the synthetic data created via create_synthetic_data.py.
  • convert_flamingo_data_moser to convert sampled flamingo data from the Moser group.
• load_data.py: Example script for how to load sub-regions from the converted data into python.

For advanced examples to segment data with a U-Net, check out the scripts folder.