Deep learning models to segment emcapsulins in 2D TEM micrograph images from the manuscript:
Genetically encoded barcodes for correlative volume electron microscopy
The models are trained on 8-bit images with a pixel size: of 0.5525 per nanometer. We include some example input and output files for replication.
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optional, but recommended: create fresh conda environment
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conda create -n emcaps_env python=3.10conda activate emcaps_env -
Clone this repository:
git clone https://github.com/HelmholtzAI-Consultants-Munich/EMcapsulins_segmentation.git
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Go into the repository and install:
cd EMcapsulins_segmentation conda create -p ./napari_submission_env python=3.10 pip install -r requirements.txt pip install -e .
run_batch_inference.py <-- Example script for inference
run_single_inference.py <-- A script that provides a method for running the network on a single image
training_script_headless.py <-- Training the model from scratch.
'''python training_script_headless.py --data_folder example_data/example_outputs --model_folder test'''
Napari viewer for labels:
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Go into the napari_plugin folder
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Start 'python napari_plugin_tif.py'
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Select the directory with images on the right hand side
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Scan directory: The program is looking at pairs of files ending with _mic.tif and _label.tif
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Select image, load it and use the annotation tools to correct the mask
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Save annotation (this updates the _label.tif file)
If you decide to use the code of this repo, please cite:
Genetically encoded barcodes for correlative volume electron microscopy, Nature Biotechnology, https://doi.org/10.1038/s41587-023-01713-y
- CUDA 11.4+
- Python 3.10+
- GPU with at least 4GB of VRAM
further details in requirements.txt
This project is licensed under the terms of the GNU Affero General Public License v3.0.
Contact us regarding licensing.
If possible please open a GitHub issue here.
For inquiries related to the code:
m.grosshauser @ tum .de
For inquiries related to the technology:
felix.sigmund @ tum .de
gil.westmeyer @ tum .de