Cell Detection with PyTorch.

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  • License: Apache Software License (Apache License, Version 2.0)
  • Author: Eric Upschulte
  • Tags cell, detection, object, segmentation, pytorch, cpn, contour, proposal, network, deep, learning, unet, fzj, julich, juelich, ai
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Project description

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)

🛠 Install

Make sure you have PyTorch installed.

PyPI

pip install -U celldetection

GitHub

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

💾 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 🔗
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()

🔬 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

🤗 Hugging Face Spaces

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

There's also an API (Python & JavaScript), allowing you to utilize community GPUs (currently Nvidia A100) remotely!

Hugging Face API

Python

from gradio_client import Client

# Define inputs (local filename or URL)
inputs = 'https://raw.githubusercontent.com/scikit-image/scikit-image/main/skimage/data/coins.png'

# Set up client
client = Client("ericup/celldetection")

# Predict
overlay_filename, img_filename, h5_filename, csv_filename = client.predict(
    inputs,  # str: Local filepath or URL of your input image
    
    # Model name
    'ginoro_CpnResNeXt101UNet-fbe875f1a3e5ce2c',
    
    # Custom Score Threshold (numeric value between 0 and 1)
    False, .9,  # bool: Whether to use custom setting; float: Custom setting
    
    # Custom NMS Threshold
    False, .3142,  # bool: Whether to use custom setting; float: Custom setting
    
    # Custom Number of Sample Points
    False, 128,  # bool: Whether to use custom setting; int: Custom setting
    
    # Overlapping objects
    True,  # bool: Whether to allow overlapping objects
    
    # API name (keep as is)
    api_name="/predict"
)


# Example usage: Code below only shows how to use the results
from matplotlib import pyplot as plt
import celldetection as cd
import pandas as pd

# Read results from local temporary files
img = imread(img_filename)
overlay = imread(overlay_filename)  # random colors per instance; transparent overlap
properties = pd.read_csv(csv_filename)
contours, scores, label_image = cd.from_h5(h5_filename, 'contours', 'scores', 'labels')

# Optionally display overlay
cd.imshow_row(img, img, figsize=(16, 9))
cd.imshow(overlay)
plt.show()

# Optionally display contours with text
cd.imshow_row(img, img, figsize=(16, 9))
cd.plot_contours(contours, texts=['score: %d%%\narea: %d' % s for s in zip((scores * 100).round(), properties.area)])
plt.show()

Javascript

import { client } from "@gradio/client";

const response_0 = await fetch("https://raw.githubusercontent.com/scikit-image/scikit-image/main/skimage/data/coins.png");
const exampleImage = await response_0.blob();
						
const app = await client("ericup/celldetection");
const result = await app.predict("/predict", [
    exampleImage,  // blob: Your input image
    
    // Model name (hosted model or URL)
    "ginoro_CpnResNeXt101UNet-fbe875f1a3e5ce2c",
    
    // Custom Score Threshold (numeric value between 0 and 1)
    false, .9,  // bool: Whether to use custom setting; float: Custom setting
    
    // Custom NMS Threshold
    false, .3142,  // bool: Whether to use custom setting; float: Custom setting
    
    // Custom Number of Sample Points
    false, 128,  // bool: Whether to use custom setting; int: Custom setting
    
    // Overlapping objects
    true,  // bool: Whether to allow overlapping objects
    
    // API name (keep as is)
    api_name="/predict"
]);

🧑‍💻 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

🏆 Awards

📝 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},
}

🔗 Links

🧑‍🔬 Thanks!

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

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for ericup from gravatar.com ericup

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: Apache Software License (Apache License, Version 2.0)
  • Author: Eric Upschulte
  • Tags cell, detection, object, segmentation, pytorch, cpn, contour, proposal, network, deep, learning, unet, fzj, julich, juelich, ai
Classifiers

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