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Cell Detection

Downloads Test PyPI

Showcase

Nuclei of U2OS cells in a chemical screen

bbbc039 https://bbbc.broadinstitute.org/BBBC039 (CC0)

P. vivax (malaria) infected human blood

bbbc041 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.

🔬 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

@misc{upschulte2021contour,
      title={Contour Proposal Networks for Biomedical Instance Segmentation}, 
      author={Eric Upschulte and Stefan Harmeling and Katrin Amunts and Timo Dickscheid},
      year={2021},
      eprint={2104.03393},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

🔗 Links