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Cell Detection

Downloads Test PyPI Documentation Status DOI

⭐ Showcase

NeurIPS 22 Cell Segmentation Competition

neurips22 https://openreview.net/forum?id=YtgRjBw-7GJ

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)

πŸ›  Install

Make sure you have PyTorch installed.

PyPI

pip install -U celldetection

GitHub

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

πŸ’Ύ Trained models

model = cd.fetch_model(model_name, check_hash=True)
model name training data link
ginoro_CpnResNeXt101UNet-fbe875f1a3e5ce2c BBBC039, BBBC038, Omnipose, Cellpose, Sartorius - Cell Instance Segmentation, Livecell, NeurIPS 22 CellSeg Challenge πŸ”—
Run a demo with a pretrained model
import torch, cv2, celldetection as cd
from skimage.data import coins
from matplotlib import pyplot as plt

# Load pretrained model
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = cd.fetch_model('ginoro_CpnResNeXt101UNet-fbe875f1a3e5ce2c', check_hash=True).to(device)
model.eval()

# Load input
img = coins()
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
print(img.dtype, img.shape, (img.min(), img.max()))

# Run model
with torch.no_grad():
    x = cd.to_tensor(img, transpose=True, device=device, dtype=torch.float32)
    x = x / 255  # ensure 0..1 range
    x = x[None]  # add batch dimension: Tensor[3, h, w] -> Tensor[1, 3, h, w]
    y = model(x)

# Show results for each batch item
contours = y['contours']
for n in range(len(x)):
    cd.imshow_row(x[n], x[n], figsize=(16, 9), titles=('input', 'contours'))
    cd.plot_contours(contours[n])
    plt.show()

πŸ”¬ Architectures

import celldetection as cd
Contour Proposal Networks
PyTorch Image Models (timm)

Also have a look at Timm Documentation.

import timm

timm.list_models(filter='*')  # explore available models
Segmentation Models PyTorch (smp)
import segmentation_models_pytorch as smp

smp.encoders.get_encoder_names()  # explore available models
encoder = cd.models.SmpEncoder(encoder_name='mit_b5', pretrained='imagenet')

Find a list of Smp Encoders in the smp documentation.

U-Nets
# U-Nets are available in 2D and 3D
import celldetection as cd

model = cd.models.ResNeXt50UNet(in_channels=3, out_channels=1, nd=3)
MA-Nets
# Many MA-Nets are available in 2D and 3D
import celldetection as cd

encoder = cd.models.ConvNeXtSmall(in_channels=3, nd=3)
model = cd.models.MaNet(encoder, out_channels=1, nd=3)
Feature Pyramid Networks
ConvNeXt Networks
# ConvNeXt Networks are available in 2D and 3D
import celldetection as cd

model = cd.models.ConvNeXtSmall(in_channels=3, nd=3)
Residual Networks
# Residual Networks are available in 2D and 3D
import celldetection as cd

model = cd.models.ResNet50(in_channels=3, nd=3)
Mobile Networks

🐳 Docker

Find us on Docker Hub: https://hub.docker.com/r/ericup/celldetection

You can pull the latest version of celldetection via:

docker pull ericup/celldetection:latest
CPN inference via Docker with GPU
docker run --rm \
  -v $PWD/docker/outputs:/outputs/ \
  -v $PWD/docker/inputs/:/inputs/ \
  -v $PWD/docker/models/:/models/ \
  --gpus="device=0" \
  celldetection:latest /bin/bash -c \
  "python cpn_inference.py --tile_size=1024 --stride=768 --precision=32-true"
CPN inference via Docker with CPU
docker run --rm \
  -v $PWD/docker/outputs:/outputs/ \
  -v $PWD/docker/inputs/:/inputs/ \
  -v $PWD/docker/models/:/models/ \
  celldetection:latest /bin/bash -c \
  "python cpn_inference.py --tile_size=1024 --stride=768 --precision=32-true --accelerator=cpu"

Apptainer

You can also pull our Docker images for the use with Apptainer (formerly Singularity) with this command:

apptainer pull --dir . --disable-cache docker://ericup/celldetection:latest

πŸ€— Hugging Face Spaces

Find us on Hugging Face and upload your own images for segmentation: https://huggingface.co/spaces/ericup/celldetection

Hugging Face API

Python

import requests

response = requests.post("https://ericup-celldetection.hf.space/run/predict", json={
    "data": [
        "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAACklEQVR4nGMAAQAABQABDQottAAAAABJRU5ErkJggg==",
        "ginoro_CpnResNeXt101UNet-fbe875f1a3e5ce2c",
    ]
}).json()

data = response["data"]

Javascript

const response = await fetch("https://ericup-celldetection.hf.space/run/predict", {
    method: "POST",
    headers: {"Content-Type": "application/json"},
    body: JSON.stringify({
        data: [
            "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAACklEQVR4nGMAAQAABQABDQottAAAAABJRU5ErkJggg==",
            "ginoro_CpnResNeXt101UNet-fbe875f1a3e5ce2c",
        ]
    })
});

const data = await response.json();

πŸ§‘β€πŸ’» Napari Plugin

Find our Napari Plugin here: https://github.com/FZJ-INM1-BDA/celldetection-napari
Find out more about Napari here: https://napari.org bbbc039 You can install it via pip:

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

πŸ† Awards

πŸ“ Citing

If you find this work useful, please consider giving a star ⭐️ and citation:

@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},
}

πŸ”— Links

πŸ§‘β€πŸ”¬ Thanks!

Stargazers repo roster for @FZJ-INM1-BDA/celldetection Forkers repo roster for @FZJ-INM1-BDA/celldetection