EMcapsulins segmentation

Deep learning models to segment emcapsulins in 2D TEM micrograph images from the manuscript:

Genetically encoded barcodes for correlative volume electron microscopy

Expected data

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.

Installation

0. optional, but recommended: create fresh conda environment

1. conda create -n emcaps_env python=3.10 conda activate emcaps_env

2. Clone this repository:

   git clone https://github.com/HelmholtzAI-Consultants-Munich/EMcapsulins_segmentation.git

3. 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 .
   

Usage

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 form scratch.

'''python training_script_headless.py --data_folder example_data/example_outputs --model_folder test'''

Napari viewer for labels:

1. Go into the napari folder

2. Start 'python napari_plugin_tif.py'

3. Select the directory with images on the right hand side

4. Scan directory: The program is looking at pairs of files ending with _mic.tif and _label.tif

5. Select image, load it and use the annotation tools to correct the mask

6. Save annotation (this updates the _label.tif file)

Citation

when using the software please cite tba

tba

Recommended Environment

• CUDA 11.4+
• Python 3.10+
• GPU with at least 4GB of VRAM

further details in requirements.txt

Licensing

This project is licensed under the terms of the GNU Affero General Public License v3.0.

Contact us regarding licensing.

Contact / Feedback / Questions

If possible please open a GitHub issue here.

For inquiries not suitable for GitHub issues:

felix.sigmund @ tum .de

gil.westmeyer @ tum .de