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README.md

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+# 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)
+- 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 will be available on bioRxiv by October 2025.  
+
+
+## 🙏 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.