README.md

# NucGen3D: A Synthetic Framework for Large-Scale 3D Nuclear Segmentation with Open-Source Training Data and Models

**NucGen3D** is an open dataset of realistically simulated, annotated 3D microscopy-like images of cell nuclei.
It was generated using a procedural simulation framework designed to reproduce the structural and visual complexity of real fluorescence microscopy data.

The dataset provides paired 3D images and ground truth masks, enabling training and benchmarking of 3D segmentation models.
It addresses the scarcity of large, well-annotated 3D datasets in bioimage analysis by offering controllable, unbiased, and reproducible training data.


## 📂 Dataset structure
This repository hosts:
- The NucGen3D dataset (40 000 3D images ≈ 10 M nuclei) together with the labelisation
- The noise templates used for augmentation and realistic image generation (Perlin and anisotropic noise)


## 🚀 What you can do
- Use the dataset for training or benchmarking 3D nuclear segmentation models
- Generate new synthetic 3D images using the NucGen3D simulator (code on GitHub)
- Augment existing data with our provided noise templates (Perlin / anisotropic) for more realistic training


## 💻 Example: loading a NucGen3D image and applying random noise
Code available on [GitHub](https://github.com/mathieuserr/nucgen3D/tree/main).
```python
from torch.utils.data import DataLoader
from nucgen3d.dataset.loader import SimImageNoiseDataset

# Example use
ds = SimImageNoiseDataset(
    img_dir="data/simulated/images",                                 # directory containing simulated images
    noise_dirs=["data/noise2", "data/noise3", "data/noise_aniso1"],  # str or list[str] - noise templates directory (.tif) - Perlin/anisotropic
    crop_size=256,                                                   # example: 256, None = full image
    z_slices=8,                                                      # example: 8 -> number of z slices
    quant_prob=0.3,                                                  # Noisator quantification parameter
    background_prob=0.7,
    background_coeff_max=0.6,
    readout_max=0.03,
    random_shot=0.5,
)

dl = DataLoader(ds, batch_size=4, shuffle=True, num_workers=0)
noisy, clean, names = next(iter(dl))
```


## Publication

📄 Preprint available on [bioRxiv](https://www.biorxiv.org/content/10.1101/2025.10.08.681092v1).  


## 🙏 Acknowledgements

This work was developed within the RESTORE (INSERM, Université de Toulouse) and IRIT (Université de Toulouse) laboratories, as part of the ANITI program and the CALM research chair.