Code to reproduce the instance segmentation results of my dissertation.

The tool microbeSEG with graphical user interface and OMERO support can be found here.

Clone the repository:

git clone https://github.com/TimScherr/DL_based_instance_segmentation_for_microscopy_images.git

Open the Anaconda Prompt (Windows) or the Terminal (Linux), go to the repository and create a new virtual environment:

cd path_to_the_cloned_repository
conda env create -f requirements.yml

Activate the virtual environment kit-sch-ge-2021_cell_segmentation_ve:

conda activate kit-sch-ge-2021_cell_segmentation_ve

Download all Cell Tracking Challenge Data (without Fluo-N3DL-DRO, Fluo-N3DL-TRIC, Fluo-N3DL-TRIF) and the evaluation software with

python download_data.py

About 40GiB free memory is needed. The training datasets with annotations are saved into ./train_data/ and the challenge data into ./challenge_data/. In addition the evaluation software will be downloaded.

This project is licensed under the MIT License - see the LICENSE.md file for details.