CellDetection 0.3.0

Cell Detection with PyTorch.

Cell Detection

⭐ Showcase

Nuclei of U2OS cells in a chemical screen

https://bbbc.broadinstitute.org/BBBC039 (CC0)

P. vivax (malaria) infected human blood

https://bbbc.broadinstitute.org/BBBC041 (CC BY-NC-SA 3.0)

🛠 Install

Make sure you have PyTorch installed.

PyPI

pip install -U celldetection

GitHub

pip install git+https://github.com/FZJ-INM1-BDA/celldetection.git

💡 How to train

Here you can see some examples of how to train a detection model. The examples already include toy data, so you can get started right away.

• Train your own model
• Train a model with multiple object classes

🔬 Models

from celldetection import models

Contour Proposal Networks:

• models.CpnU22
• models.CpnSlimU22
• models.CpnWideU22
• models.CpnResNet18FPN
• models.CpnResNet34FPN
• models.CpnResNet50FPN
• models.CpnResNet101FPN
• models.CpnResNet152FPN
• models.CpnResNeXt50FPN
• models.CpnResNeXt101FPN
• models.CpnResNeXt152FPN
• models.CpnWideResNet50FPN
• models.CpnWideResNet101FPN
• models.CpnMobileNetV3SmallFPN
• models.CpnMobileNetV3LargeFPN
• models.CPN

U-Nets:

• models.U22
• models.SlimU22
• models.WideU22
• models.U17
• models.U12
• models.UNetEncoder
• models.UNet

Feature Pyramid Networks:

• models.ResNet18FPN
• models.ResNet34FPN
• models.ResNet50FPN
• models.ResNet101FPN
• models.ResNet152FPN
• models.ResNeXt50FPN
• models.ResNeXt101FPN
• models.ResNeXt152FPN
• models.WideResNet50FPN
• models.WideResNet101FPN
• models.MobileNetV3SmallFPN
• models.MobileNetV3LargeFPN
• models.FPN

Residual Networks:

• models.ResNet18
• models.ResNet34
• models.ResNet50
• models.ResNet101
• models.ResNet152
• models.ResNeXt50_32x4d
• models.ResNeXt101_32x8d
• models.ResNeXt152_32x8d
• models.WideResNet50_2
• models.WideResNet101_2

Mobile Networks:

• models.MobileNetV3Small
• models.MobileNetV3Large

📝 Citing

@article{UPSCHULTE2022102371,
    title = {Contour proposal networks for biomedical instance segmentation},
    journal = {Medical Image Analysis},
    volume = {77},
    pages = {102371},
    year = {2022},
    issn = {1361-8415},
    doi = {https://doi.org/10.1016/j.media.2022.102371},
    url = {https://www.sciencedirect.com/science/article/pii/S136184152200024X},
    author = {Eric Upschulte and Stefan Harmeling and Katrin Amunts and Timo Dickscheid},
    keywords = {Cell detection, Cell segmentation, Object detection, CPN},
}

🔗 Links

• Article (sciencedirect)
• PDF (sciencedirect)
• Abstract (arXiv)
• PyPI
• Documentation