Cell Detection
β 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)
π Install
Make sure you have PyTorch installed.
PyPI
pip install -U celldetection
GitHub
pip install git+https://github.com/FZJ-INM1-BDA/celldetection.git
π‘ 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.
π¬ Models
import celldetection as cd
Contour Proposal Networks
cd.models.CPN
cd.models.CpnU22
cd.models.CpnSlimU22
cd.models.CpnResUNet
cd.models.CpnWideU22
cd.models.CpnResNet34FPN
cd.models.CpnResNet50FPN
cd.models.CpnResNet18FPN
cd.models.CpnResNeXt50FPN
cd.models.CpnResNet101FPN
cd.models.CpnResNet152FPN
cd.models.CpnResNeXt101FPN
cd.models.CpnResNeXt152FPN
cd.models.CpnWideResNet50FPN
cd.models.CpnWideResNet101FPN
cd.models.CpnMobileNetV3SmallFPN
cd.models.CpnMobileNetV3LargeFPN
U-Nets
cd.models.U22
cd.models.U17
cd.models.U12
cd.models.UNet
cd.models.WideU22
cd.models.SlimU22
cd.models.ResUNet
cd.models.UNetEncoder
cd.models.ResNet50UNet
cd.models.ResNet18UNet
cd.models.ResNet34UNet
cd.models.ResNet152UNet
cd.models.ResNet101UNet
cd.models.ResNeXt50UNet
cd.models.ResNeXt152UNet
cd.models.ResNeXt101UNet
cd.models.WideResNet50UNet
cd.models.WideResNet101UNet
cd.models.MobileNetV3SmallUNet
cd.models.MobileNetV3LargeUNet
Feature Pyramid Networks
cd.models.FPN
cd.models.ResNet18FPN
cd.models.ResNet34FPN
cd.models.ResNet50FPN
cd.models.ResNeXt50FPN
cd.models.ResNet101FPN
cd.models.ResNet152FPN
cd.models.ResNeXt101FPN
cd.models.ResNeXt152FPN
cd.models.WideResNet50FPN
cd.models.WideResNet101FPN
cd.models.MobileNetV3LargeFPN
cd.models.MobileNetV3SmallFPN
Residual Networks
cd.models.ResNet18
cd.models.ResNet34
cd.models.ResNet50
cd.models.ResNet101
cd.models.ResNet152
cd.models.WideResNet50_2
cd.models.ResNeXt50_32x4d
cd.models.WideResNet101_2
cd.models.ResNeXt101_32x8d
cd.models.ResNeXt152_32x8d
Mobile Networks
π 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},
}