The Mutex Watershed algorithm for efficient segmentation without seeds. For the corresponding publication see: http://openaccess.thecvf.com/content_ECCV_2018/html/Steffen_Wolf_The_Mutex_Watershed_ECCV_2018_paper.html
On Unix (Linux, OS X)
- create conda env with xtensor:
conda create -n mws -c conda-forge xtensor-python- activate the env:
source activate mws- clone this repository, enter it and install:
python setup.py install
To reproduce the ISBI experiments, go to the experiments/isbi folder
and run the isbi_experiments script:
python isbi_experiments.py /path/to/raw.h5 /path/to/affinities.h5 /path/to/res_folder --algorithms mws
You will need vigra as additional dependency:
conda install -c conda-forge vigra
You can also reproduce the baseline results by specifying further algorithms. Note that most of these will need the https://github.com/constantinpape/cremi_tools repository and further dependencies specified there.
You can obtain the data from: https://hcicloud.iwr.uni-heidelberg.de/index.php/s/6LuE7nxBN3EFRtL
To train an affinity network for isbi, you can use the scripts in experiments/training.
You will also need inferno and neurofire