Update README.md

README.md

@@ -1,58 +1,64 @@
-<h2 align="center"> Kartezio </h2>
-<h5 align="center"> A Darwinian Designer of Explainable Algorithms for Biomedical Image Segmentation </h5>
+<h2 align="center">Evolutionary design of explainable algorithms for biomedical image segmentation</h2>
+<h4 align="center">Kartezio Official Python Package</h4>
 
-## Package Description
-[TODO]
-
-## Installation
+---
 
-Tested on Windows, Ubuntu 18.04, Ubuntu 22.04.
+**Kartezio** is a modular Cartesian Genetic Programming (CGP) framework that enables the automated design of fully interpretable image-processing pipelines, without the need for GPUs or extensive training datasets. Built on top of [OpenCV](https://opencv.org/), Kartezio empowers researchers, engineers, and practitioners to discover novel computer vision (CV) solutions using only a handful of annotated samples and a single CPU core.
 
-Tested with different versions of Python3: 3.7, 3.8, 3.9 and 3.10.
+Originally developed for biomedical image segmentation, Kartezio has been successfully showcased in [Nature Communications](https://www.nature.com/articles/s41467-023-42664-x). Although it shines in medical and life science applications, Kartezio’s underlying principles are domain-agnostic. Whether you’re working with industrial quality control, satellite imagery, embedded vision, or robotics, Kartezio helps you craft custom CV pipelines that are **transparent, fast, frugal  and efficient**.
 
+## Key Features
 
-### Creation of a virtualenv is recommanded:
+:nut_and_bolt: **Modular and Customizable**  
+   Kartezio is built from interchangeable building blocks, called **Components**, that you can mix, match, or replace. Adapt the pipeline to your project’s unique requirements.
 
-```bash
-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
-```
+:pencil2: **Few-Shot Learning**  
+   Forget the need for massive, annotated datasets. Kartezio can evolve solutions from just a few annotated examples, saving both time and computational resources.
 
-### Installation from Pypi
+:white_check_mark: **Transparent and Certifiable**
+   Every pipeline produced is fully transparent. Inspect the exact operations used, understand their sequence, and trust the decisions made by your model.
 
-```bash
-(venv_name)$ pip install kartezio
-```
+:earth_africa: **Frugal and Local**  
+   Run everything on a single CPU, without GPUs or massive compute clusters. This makes Kartezio ideal for edge devices, embedded systems, or scenarios with limited computational resources.
 
-### Local installation using pip
+:microscope: **Broad Applicability**  
+   While proven in biomedical image segmentation, Kartezio’s methods readily extend to other fields—like industrial machine vision, space imaging, drone footage analysis, or any custom image-based problem.
 
-```bash
-(venv_name)$ git clone [TODO] 
-(venv_name)$ cd kartezio
-(venv_name)$ python -m pip install -e .
-```
-## First steps
-[TODO]
+## Getting Started
 
-## 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:
+1. **Installation:**
+   ```bash
+   pip install kartezio
 
-|                  | 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 |
+2. **First steps**
+[TODO]
 
-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
-[TODO .bibtex]
+If you use Kartezio in your research, please consider citing:
+```
+@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}
+}
+```
+If you are using the multimodal version of Kartezio, please also cite:
+```
+@inproceedings{de2024multimodal,
+  title={Multimodal adaptive graph evolution},
+  author={De La Torre, Camilo and Cortacero, K{\'e}vin and Cussat-Blanc, Sylvain and Wilson, Dennis},
+  booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion},
+  pages={499--502},
+  year={2024}
+}
+```
 
 
 ## Licensing