Evolutionary design of explainable algorithms for biomedical image segmentation

Kartezio Official Python Package

Package Description

Kartezio is a modular Cartesian Genetic Programming framework that generates fully transparent and easily interpretable image processing pipelines.

Link to retated python scripts: Kartezio scripts
Link to retated datasets and trained models: Datasets & Trained Models
Link to include the cellpose dataset: Cellpose Dataset
Link to publication: Evolutionary design of explainable algorithms for biomedical image segmentation
Link to official website: kartezio.com

Installation

Tested on Windows, Ubuntu 18.04, Ubuntu 22.04.

Tested with different versions of Python3: 3.7, 3.8, 3.9 and 3.10.

Creation of a virtualenv is recommanded:

python3 -m pip install virtualenv
python3 -m venv <path/to/venv/venv_name>
source <path/to/venv/venv_name>/bin/activate
pip install --upgrade pip

Installation from Pypi

(venv_name)$ pip install kartezio

Local installation using pip

(venv_name)$ git clone https://github.com/KevinCortacero/Kartezio.git
(venv_name)$ cd kartezio
(venv_name)$ python -m pip install -e .

First steps

[TODO]

Reported Results

Kartezio was compared on the Cell Image Library dataset against the reported performance of Cellpose/Stardist/MRCNN (December, 2022) as reported in Stringer et al, Nature Methods, 2021 and published in Cortacero et al, Nature Communnications, 2023:

	Kartezio	Kartezio	Kartezio	Cellpose	Stardist	MRCNN
Training images	8	50	89	89	89	89
AP50 on test set	0.838 (mean)	0.849 (mean)	0.858 (mean)	0.91 (max)	0.76 (max)	0.80 (max)

An additional, but not published, comparison was performed against the reported performance of CPP-Net, on BBBC006v1 dataset reported in Chen et al, 2023 (July, 2023):

	Kartezio-s1	Kartezio-s2	CPP-Net	Stardist
Training images	20	20	538	538
AP50 on test set	0.822	0.879	0.9811	0.9757

References and Citation

@article{cortacero2023evolutionary,
  title={Evolutionary design of explainable algorithms for biomedical image segmentation},
  author={Cortacero, K{\'e}vin and McKenzie, Brienne and M{\"u}ller, Sabina and Khazen, Roxana and Lafouresse, Fanny and Corsaut, Ga{\"e}lle and Van Acker, Nathalie and Frenois, Fran{\c{c}}ois-Xavier and Lamant, Laurence and Meyer, Nicolas and others},
  journal={Nature Communications},
  volume={14},
  number={1},
  pages={7112},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

Licensing

The Software is freely available for Non-Commercial and Academic purposes only, under the terms and conditions set out in the License file, and You may not use the Software except in compliance with the License. The Software distributed under the License is distributed on an "as is" basis, without warranties or conditions of any kind, either express or implied. See the License file for the specific language governing permissions and limitations under the License.