This is the official code repository for our paper accepted at ICML 2025: The Four Color Theorem for Cell Instance Segmentation

• Paper Link: Read the Paper
• Publication: International Conference on Machine Learning (ICML) 2025
• Authors: Ye Zhang, Yu Zhou, Yifeng Wang, Jun Xiao, Ziyue Wang, Yongbing Zhang, Jianxu Chen

• Implementation of a novel approach applying graph coloring principles (inspired by the Four Color Theorem) for consistent cell instance visualization or downstream tasks.
• Integration with popular computer vision frameworks (mmsegmentation, Detectron2) for robust instance segmentation and training pipelines.
• Support for processing various biomedical image datasets (BBBC006, DSB2018, PanNuke, Yeaz).

This project requires Python 3.7 and was developed with specific versions of PyTorch and MMCV.

1. Create a Conda environment:

conda create -n fcis python=3.7 -y
conda activate fcis

2. Install PyTorch:

Ensure you install the version compatible with your CUDA version. The original development used CUDA 11.1.

# For CUDA 11.1
pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html

3. Install MMCV-full:

Installing mmcv-full with CUDA support is crucial for leveraging GPU acceleration with the OpenMMLab frameworks.

pip install mmcv-full==1.3.13

4. Install Required Packages:

Install the remaining dependencies listed in the requirements.txt file.

pip install -r requirements.txt

5. Clone and Install this repository:

git clone https://github.com/zhangye-zoe/FCIS.git
cd FCIS
pip install -e .

This project utilizes data from the following publicly available datasets:

• BBBC006v1: https://bbbc.broadinstitute.org/BBBC006
• DSB2018: https://www.kaggle.com/competitions/data-science-bowl-2018/data
• PanNuke: https://warwick.ac.uk/fac/sci/dcs/research/tia/data/pannuke/
• Yeaz: https://www.epfl.ch/labs/lpbs/data-and-software/

Preprocessing is required to convert raw datasets into the format expected by our training and evaluation pipelines.

For detailed instructions on how to download and preprocess the data, please refer to:

📄 Data Preparation Guide

This guide includes information on the required data structure and the scripts/notebooks located in the ./preprocessing/ directory.

Download the pre-trained model weights from: 👉 Hugging Face – FCIS Weights

To quickly evaluate the trained model and visualize the prediction results, run the following command:

python tools/test.py \
configs/FCIS/fcis_bbbc.py \
./work_dirs/fcis_bbbc/fcis_bbbc.pth \
--show
-show-fold ./z_visual/BBBC

python tools/train.py \
configs/FCIS/fcis_bbbc.py

python tools/test.py \
configs/FCIS/fcis_bbbc.py \
./work_dirs/fcis_bbbc/latest.pth \

If you find our work useful, please cite our paper:

@inproceedings{zhang2025fourcolor,
  title={The Four Color Theorem For Cell Instance Segmentation},
  author={Ye Zhang and Yu Zhou and Yifeng Wang and Jun Xiao and Ziyue Wang and Yongbing Zhang and Jianxu Chen},
  booktitle={International Conference on Machine Learning (ICML)},
  year={2025}
}