bbbc-datasets 0.1.4

PyTorch-compatible dataset manager for BBBC datasets

BBBC Datasets - PyTorch Dataset Manager

📌 Overview

This package provides a PyTorch-compatible dataset manager for Broad Bioimage Benchmark Collection (BBBC) datasets.

It allows users to:

• Download and manage BBBC datasets automatically
• Load datasets as PyTorch Dataset objects
• Handle 2D & 3D images properly
• Support image augmentations via torchvision.transforms (#TODO)

🚀 Installation

Install from PyPI

pip install bbbc-datasets

Install from Source

Clone the Repository

git clone https://github.com/mario-koddenbrock/bbbc_datasets.git
cd bbbc_datasets

Install Dependencies

pip install -r requirements.txt

Dependencies:

• torch, torchvision (Deep Learning Framework)
• numpy, tifffile, opencv-python (Image Handling)
• requests, matplotlib (Downloading & Visualization)

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📂 Available Datasets

You can list all available datasets without downloading them:

python examples/load_dataset.py

Output

📂 Available BBBC Datasets:
- BBBC003: DIC microscopy images of mouse embryos for segmentation analysis.
- BBBC004: Synthetic fluorescence microscopy images of nuclei with varying clustering probabilities.
- BBBC005: Simulated fluorescence microscopy images with varying focus blur.
- BBBC006: Z-stack fluorescence microscopy images of human U2OS cells.
- BBBC008: Dataset: Fluorescence microscopy images of human HT29 colon cancer cells.
- BBBC010: Live/dead assay of C. elegans exposed to pathogens.
- BBBC024: 3D Synthetic HL60 Cell Line Images with Varying Clustering Probability and SNR.
- BBBC027: 3D Synthetic Colon Tissue Images with Varying SNR.
- BBBC028: Polymerized Structures in Differential Interference Contrast (DIC) Microscopy.
- BBBC029: Synthetic Differential Interference Contrast (DIC) Images.
- BBBC032: 3D Mouse Embryo Blastocyst Cells.
- BBBC033: 3D Mouse Trophoblast Stem Cells.
- BBBC034: 3D Induced Pluripotent Human Stem Cells (hiPSC).
- BBBC035: Simulated HL60 Cells from the Cell Tracking Challenge.
- BBBC038: Kaggle 2018 Data Science Bowl - Nuclei Segmentation.
- BBBC039: Nuclei of U2OS Cells in a Chemical Screen.
- BBBC046: FiloData3D - Synthetic 3D Time-Lapse Imaging of A549 Cells with Filopodia.
- BBBC050: Nuclei of Mouse Embryonic Cells.

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🎨 Visualizing Dataset Samples

Display First Image from Each Dataset

Requires downloading datasets first!

python examples/load_dataset.py

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📥 Download and Load a Dataset

Load a Dataset as a PyTorch Dataset

from bbbc_datasets.datasets.bbbc004 import BBBC004
from torch.utils.data import DataLoader

# Load BBBC004 dataset
dataset = BBBC004()

# Create a DataLoader
dataloader = DataLoader(dataset, batch_size=4, shuffle=True)

# Iterate through data
for images, labels in dataloader:
    print(f"Batch shape: {images.shape}, Labels: {labels.shape if labels is not None else 'None'}")

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🛠 Running Tests

Validate Dataset URLs

Checks if all dataset URLs are reachable 🌍

python -m unittest tests/test_dataset_urls.py

Verify Dataset Loading

Ensures datasets can be loaded and accessed correctly 🛠

python -m unittest tests/test_dataset_loading.py

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📝 Up-next

• Thinking of what is up next
• Maybe integrating other datasets as cellpose, livecell, etc.

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🙌 Contributing

Contributions are welcome! If you find issues or want to add new features, feel free to:

• Submit a Pull Request
• Report Issues in the GitHub Issues section

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📜 License

This project is licensed under the MIT License.