https://github.com/fzj-inm1-bda/celldetection

Scalable Instance Segmentation using PyTorch & PyTorch Lightning.
https://github.com/fzj-inm1-bda/celldetection
cell-counting cell-detection cell-segmentation celldetection cpn deep-learning fpn instance-segmentation mask-rcnn object-detection pytorch semantic-segmentation unet
Last synced: 4 months ago
JSON representation
Scalable Instance Segmentation using PyTorch & PyTorch Lightning.
Host: GitHub
URL: https://github.com/fzj-inm1-bda/celldetection
Owner: FZJ-INM1-BDA
License: apache-2.0
Created: 2021-03-18T14:49:03.000Z (over 4 years ago)
Default Branch: main
Last Pushed: 2025-04-22T15:50:43.000Z (6 months ago)
Last Synced: 2025-05-24T13:05:27.375Z (5 months ago)
Topics: cell-counting, cell-detection, cell-segmentation, celldetection, cpn, deep-learning, fpn, instance-segmentation, mask-rcnn, object-detection, pytorch, semantic-segmentation, unet
Language: Python
Homepage: https://docs.celldetection.org
Size: 15.4 MB
Stars: 139
Watchers: 5
Forks: 21
Open Issues: 14
Metadata Files:
- Readme: README.md
- License: LICENSE
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README

          # Cell Detection

[![Downloads](https://static.pepy.tech/badge/celldetection?l)](https://pepy.tech/project/celldetection)

[![Test](https://github.com/FZJ-INM1-BDA/celldetection/workflows/Test/badge.svg)](https://github.com/FZJ-INM1-BDA/celldetection/actions?query=workflow%3ATest)

[![PyPI](https://img.shields.io/pypi/v/celldetection?l)](https://pypi.org/project/celldetection/)

[![Documentation Status](https://readthedocs.org/projects/celldetection/badge/?version=latest)](https://celldetection.readthedocs.io/en/latest/?badge=latest)

[![DOI](https://zenodo.org/badge/349111085.svg)](https://zenodo.org/badge/latestdoi/349111085)

## ⭐ Showcase

###### NeurIPS 22 Cell Segmentation Competition

![neurips22](https://raw.githubusercontent.com/FZJ-INM1-BDA/celldetection/main/assets/neurips-cellseg-demo.png "NeurIPS 22 Cell Segmentation Competition - Find more information here: https://neurips.cc/Conferences/2022/CompetitionTrack")

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

###### Nuclei of U2OS cells in a chemical screen

![bbbc039](https://raw.githubusercontent.com/FZJ-INM1-BDA/celldetection/main/assets/bbbc039-cpn-u22-demo.png "BBBC039 demo with CpnU22 - Find the dataset here: https://bbbc.broadinstitute.org/BBBC039")

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

###### P. vivax (malaria) infected human blood

![bbbc041](https://raw.githubusercontent.com/FZJ-INM1-BDA/celldetection/main/assets/bbbc041-cpn-u22-demo.png "BBBC041 demo with CpnU22 - Find the dataset here: https://bbbc.broadinstitute.org/BBBC041")

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

## 🛠 Install

Make sure you have [PyTorch](https://pytorch.org/get-started/locally/) installed.

### PyPI

```

pip install -U celldetection

```

### GitHub

```

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

```

## 💾 Trained models

```python

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 | [🔗](https://celldetection.org/torch/models/ginoro_CpnResNeXt101UNet-fbe875f1a3e5ce2c.pt) |

  Run a demo with a pretrained model

```python

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

```python

import celldetection as cd

```

  Contour Proposal Networks

- [`cd.models.CPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CPN)

- [`cd.models.CpnU22`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnU22)

- [`cd.models.CPNCore`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CPNCore)

- [`cd.models.CpnResUNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnResUNet)

- [`cd.models.CpnSlimU22`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnSlimU22)

- [`cd.models.CpnWideU22`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnWideU22)

- [`cd.models.CpnResNet18FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnResNet18FPN)

- [`cd.models.CpnResNet34FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnResNet34FPN)

- [`cd.models.CpnResNet50FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnResNet50FPN)

- [`cd.models.CpnResNeXt50FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnResNeXt50FPN)

- [`cd.models.CpnResNet101FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnResNet101FPN)

- [`cd.models.CpnResNet152FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnResNet152FPN)

- [`cd.models.CpnResNet18UNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnResNet18UNet)

- [`cd.models.CpnResNet34UNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnResNet34UNet)

- [`cd.models.CpnResNet50UNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnResNet50UNet)

- [`cd.models.CpnResNeXt101FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnResNeXt101FPN)

- [`cd.models.CpnResNeXt152FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnResNeXt152FPN)

- [`cd.models.CpnResNeXt50UNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnResNeXt50UNet)

- [`cd.models.CpnResNet101UNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnResNet101UNet)

- [`cd.models.CpnResNet152UNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnResNet152UNet)

- [`cd.models.CpnResNeXt101UNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnResNeXt101UNet)

- [`cd.models.CpnResNeXt152UNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnResNeXt152UNet)

- [`cd.models.CpnWideResNet50FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnWideResNet50FPN)

- [`cd.models.CpnWideResNet101FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnWideResNet101FPN)

- [`cd.models.CpnMobileNetV3LargeFPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnMobileNetV3LargeFPN)

- [`cd.models.CpnMobileNetV3SmallFPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnMobileNetV3SmallFPN)

  PyTorch Image Models (timm)

Also have a look at [Timm Documentation](https://huggingface.co/docs/timm/index).

```python

import timm

timm.list_models(filter='*')  # explore available models

```

- [`cd.models.CpnTimmMaNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnTimmMaNet)

- [`cd.models.CpnTimmUNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnTimmUNet)

- [`cd.models.TimmEncoder`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.timmodels.TimmEncoder)

- [`cd.models.TimmFPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.fpn.TimmFPN)

- [`cd.models.TimmMaNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.manet.TimmMaNet)

- [`cd.models.TimmUNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.TimmUNet)

  Segmentation Models PyTorch (smp)

```python

import segmentation_models_pytorch as smp

smp.encoders.get_encoder_names()  # explore available models

```

```python

encoder = cd.models.SmpEncoder(encoder_name='mit_b5', pretrained='imagenet')

```

Find a list of [Smp Encoders](https://smp.readthedocs.io/en/latest/encoders.html) in the `smp` documentation.

- [`cd.models.CpnSmpMaNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnSmpMaNet)

- [`cd.models.CpnSmpUNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.cpn.CpnSmpUNet)

- [`cd.models.SmpEncoder`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.smp.SmpEncoder)

- [`cd.models.SmpFPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.fpn.SmpFPN)

- [`cd.models.SmpMaNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.manet.SmpMaNet)

- [`cd.models.SmpUNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.SmpUNet)

    U-Nets

```python

# U-Nets are available in 2D and 3D

import celldetection as cd

model = cd.models.ResNeXt50UNet(in_channels=3, out_channels=1, nd=3)

```

- [`cd.models.U22`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.U22)

- [`cd.models.U17`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.U17)

- [`cd.models.U12`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.U12)

- [`cd.models.UNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.UNet)

- [`cd.models.WideU22`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.WideU22)

- [`cd.models.SlimU22`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.SlimU22)

- [`cd.models.ResUNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.ResUNet)

- [`cd.models.UNetEncoder`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.UNetEncoder)

- [`cd.models.ResNet50UNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.ResNet50UNet)

- [`cd.models.ResNet18UNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.ResNet18UNet)

- [`cd.models.ResNet34UNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.ResNet34UNet)

- [`cd.models.ResNet152UNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.ResNet152UNet)

- [`cd.models.ResNet101UNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.ResNet101UNet)

- [`cd.models.ResNeXt50UNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.ResNeXt50UNet)

- [`cd.models.ResNeXt152UNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.ResNeXt152UNet)

- [`cd.models.ResNeXt101UNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.ResNeXt101UNet)

- [`cd.models.WideResNet50UNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.WideResNet50UNet)

- [`cd.models.WideResNet101UNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.WideResNet101UNet)

- [`cd.models.MobileNetV3SmallUNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.MobileNetV3SmallUNet)

- [`cd.models.MobileNetV3LargeUNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.unet.MobileNetV3LargeUNet)

    MA-Nets

```python

# 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)

```

- [`cd.models.MaNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.manet.MaNet)

- [`cd.models.SmpMaNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.manet.SmpMaNet)

- [`cd.models.TimmMaNet`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.manet.TimmMaNet)

    Feature Pyramid Networks

- [`cd.models.FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.fpn.FPN)

- [`cd.models.ResNet18FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.fpn.ResNet18FPN)

- [`cd.models.ResNet34FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.fpn.ResNet34FPN)

- [`cd.models.ResNet50FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.fpn.ResNet50FPN)

- [`cd.models.ResNeXt50FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.fpn.ResNeXt50FPN)

- [`cd.models.ResNet101FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.fpn.ResNet101FPN)

- [`cd.models.ResNet152FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.fpn.ResNet152FPN)

- [`cd.models.ResNeXt101FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.fpn.ResNeXt101FPN)

- [`cd.models.ResNeXt152FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.fpn.ResNeXt152FPN)

- [`cd.models.WideResNet50FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.fpn.WideResNet50FPN)

- [`cd.models.WideResNet101FPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.fpn.WideResNet101FPN)

- [`cd.models.MobileNetV3LargeFPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.fpn.MobileNetV3LargeFPN)

- [`cd.models.MobileNetV3SmallFPN`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.fpn.MobileNetV3SmallFPN)

    ConvNeXt Networks

```python

# ConvNeXt Networks are available in 2D and 3D

import celldetection as cd

model = cd.models.ConvNeXtSmall(in_channels=3, nd=3)

```

- [`cd.models.ConvNeXt`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.convnext.MaNet)

- [`cd.models.ConvNeXtTiny`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.convnext.ConvNeXtTiny)

- [`cd.models.ConvNeXtSmall`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.convnext.ConvNeXtSmall)

- [`cd.models.ConvNeXtBase`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.convnext.ConvNeXtBase)

- [`cd.models.ConvNeXtLarge`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.convnext.ConvNeXtLarge)

    Residual Networks

```python

# Residual Networks are available in 2D and 3D

import celldetection as cd

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

```

- [`cd.models.ResNet18`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.resnet.ResNet18)

- [`cd.models.ResNet34`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.resnet.ResNet34)

- [`cd.models.ResNet50`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.resnet.ResNet50)

- [`cd.models.ResNet101`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.resnet.ResNet101)

- [`cd.models.ResNet152`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.resnet.ResNet152)

- [`cd.models.WideResNet50_2`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.resnet.WideResNet50_2)

- [`cd.models.ResNeXt50_32x4d`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.resnet.ResNeXt50_32x4d)

- [`cd.models.WideResNet101_2`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.resnet.WideResNet101_2)

- [`cd.models.ResNeXt101_32x8d`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.resnet.ResNeXt101_32x8d)

- [`cd.models.ResNeXt152_32x8d`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.resnet.ResNeXt152_32x8d)

    Mobile Networks

- [`cd.models.MobileNetV3Large`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.mobilenetv3.MobileNetV3Large)

- [`cd.models.MobileNetV3Small`](https://docs.celldetection.org/en/latest/celldetection.models.html#celldetection.models.mobilenetv3.MobileNetV3Small)

## 🐳 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](https://apptainer.org/) (formerly [Singularity](https://github.com/apptainer/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

```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

```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](https://raw.githubusercontent.com/FZJ-INM1-BDA/celldetection-napari/main/assets/coins-demo.png "Napari Plugin")

You can install it via pip:

```

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

```

## 🏆 Awards

- [NeurIPS 2022 Cell Segmentation Challenge](https://neurips22-cellseg.grand-challenge.org/): Winner Finalist Award

## 📝 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

- [Article (sciencedirect)](https://www.sciencedirect.com/science/article/pii/S136184152200024X "Contour Proposal Networks for Biomedical Instance Segmentation")

- [PDF (sciencedirect)](https://www.sciencedirect.com/science/article/pii/S136184152200024X/pdfft "Contour Proposal Networks for Biomedical Instance Segmentation")

- [PyPI](https://pypi.org/project/celldetection/ "CellDetection")

- [Documentation](https://docs.celldetection.org "Documentation")

## 🧑‍🔬 Thanks!

[![Stargazers repo roster for @FZJ-INM1-BDA/celldetection](http://reporoster.com/stars/FZJ-INM1-BDA/celldetection)](https://github.com/FZJ-INM1-BDA/celldetection/stargazers)

[![Forkers repo roster for @FZJ-INM1-BDA/celldetection](http://reporoster.com/forks/FZJ-INM1-BDA/celldetection)](https://github.com/FZJ-INM1-BDA/celldetection/network/members)
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