

# Model Card for cell-seg-sribd

<!-- Provide a quick summary of what the model is/does. -->

This repository provides the solution of team Sribd-med for NeurIPS-CellSeg Challenge. The details of our method are described in our paper [Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images]. Some parts of the codes are from the baseline codes of the NeurIPS-CellSeg-Baseline repository,

You can reproduce our method as follows step by step:


### How to Get Started with the Model

Install requirements by python -m pip install -r requirements.txt

## Training Details

### Training Data

The competition training and tuning data can be downloaded from https://neurips22-cellseg.grand-challenge.org/dataset/ Besides, you can download three publiced data from the following link: Cellpose: https://www.cellpose.org/dataset  Omnipose: http://www.cellpose.org/dataset_omnipose Sartorius: https://www.kaggle.com/competitions/sartorius-cell-instance-segmentation/overview 

## Environments and Requirements:
Install requirements by

```shell
python -m pip install -r requirements.txt
```

### How to use

Here is how to use this model:

```python


from skimage import io, segmentation, morphology, measure, exposure
from sribd_cellseg_models import MultiStreamCellSegModel,ModelConfig
import numpy as np
import tifffile as tif
import requests
import torch
from PIL import Image
from overlay import visualize_instances_map
import cv2
img_name = 'test_images/cell_00551.tiff'

def normalize_channel(img, lower=1, upper=99):
    non_zero_vals = img[np.nonzero(img)]
    percentiles = np.percentile(non_zero_vals, [lower, upper])
    if percentiles[1] - percentiles[0] > 0.001:
        img_norm = exposure.rescale_intensity(img, in_range=(percentiles[0], percentiles[1]), out_range='uint8')
    else:
        img_norm = img
    return img_norm.astype(np.uint8)
if img_name.endswith('.tif') or img_name.endswith('.tiff'):
    img_data = tif.imread(img_name)
else:
    img_data = io.imread(img_name)
        # normalize image data
if len(img_data.shape) == 2:
    img_data = np.repeat(np.expand_dims(img_data, axis=-1), 3, axis=-1)
elif len(img_data.shape) == 3 and img_data.shape[-1] > 3:
    img_data = img_data[:,:, :3]
else:
    pass
pre_img_data = np.zeros(img_data.shape, dtype=np.uint8)
for i in range(3):
    img_channel_i = img_data[:,:,i]
    if len(img_channel_i[np.nonzero(img_channel_i)])>0:
        pre_img_data[:,:,i] = normalize_channel(img_channel_i, lower=1, upper=99)
#dummy_input = np.zeros((512,512,3)).astype(np.uint8)
my_model = MultiStreamCellSegModel.from_pretrained("Lewislou/cellseg_sribd")
checkpoints = torch.load('model.pt')
my_model.__init__(ModelConfig())
my_model.load_checkpoints(checkpoints)
with torch.no_grad():
    output = my_model(pre_img_data)
overlay = visualize_instances_map(pre_img_data,star_label)
cv2.imwrite('prediction.png', cv2.cvtColor(overlay, cv2.COLOR_RGB2BGR))


```

## Citation
If any part of this code is used, please acknowledge it appropriately and cite the paper:
```bibtex
@misc{
lou2022multistream,
title={Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images},
author={WEI LOU and Xinyi Yu and Chenyu Liu and Xiang Wan and Guanbin Li and Siqi Liu and Haofeng Li},
year={2022},
url={https://openreview.net/forum?id=G24BybwKe9}
}
```
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["cell segmentation", "stardist", "hover-net"], "datasets": ["Lewislou/cell_samples"], "metrics": ["f1"], "pipeline_tag": "image-segmentation"}
transformers pytorch cell_sribd cell segmentation stardist hover-net image-segmentation en dataset:Lewislou/cell_samples license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #cell_sribd #cell segmentation #stardist #hover-net #image-segmentation #en #dataset-Lewislou/cell_samples #license-apache-2.0 #endpoints_compatible #has_space #region-us 



# Model Card for cell-seg-sribd



This repository provides the solution of team Sribd-med for NeurIPS-CellSeg Challenge. The details of our method are described in our paper [Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images]. Some parts of the codes are from the baseline codes of the NeurIPS-CellSeg-Baseline repository,

You can reproduce our method as follows step by step:


### How to Get Started with the Model

Install requirements by python -m pip install -r URL

## Training Details

### Training Data

The competition training and tuning data can be downloaded from URL Besides, you can download three publiced data from the following link: Cellpose: URL  Omnipose: URL Sartorius: URL 

## Environments and Requirements:
Install requirements by



### How to use

Here is how to use this model:



If any part of this code is used, please acknowledge it appropriately and cite the paper:

# Model Card for cell-seg-sribd



This repository provides the solution of team Sribd-med for NeurIPS-CellSeg Challenge. The details of our method are described in our paper [Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images]. Some parts of the codes are from the baseline codes of the NeurIPS-CellSeg-Baseline repository,

You can reproduce our method as follows step by step: ### How to Get Started with the Model

Install requirements by python -m pip install -r URL ## Training Details ### Training Data

The competition training and tuning data can be downloaded from URL Besides, you can download three publiced data from the following link: Cellpose: URL  Omnipose: URL Sartorius: URL ## Environments and Requirements:
Install requirements by ### How to use

Here is how to use this model:



If any part of this code is used, please acknowledge it appropriately and cite the paper:
TAGS
#transformers #pytorch #cell_sribd #cell segmentation #stardist #hover-net #image-segmentation #en #dataset-Lewislou/cell_samples #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 # Model Card for cell-seg-sribd



This repository provides the solution of team Sribd-med for NeurIPS-CellSeg Challenge. The details of our method are described in our paper [Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images]. Some parts of the codes are from the baseline codes of the NeurIPS-CellSeg-Baseline repository,

You can reproduce our method as follows step by step: ### How to Get Started with the Model

Install requirements by python -m pip install -r URL ## Training Details ### Training Data

The competition training and tuning data can be downloaded from URL Besides, you can download three publiced data from the following link: Cellpose: URL  Omnipose: URL Sartorius: URL ## Environments and Requirements:
Install requirements by ### How to use

Here is how to use this model:



If any part of this code is used, please acknowledge it appropriately and cite the paper:
TAGS
#transformers #pytorch #cell_sribd #cell segmentation #stardist #hover-net #image-segmentation #en #dataset-Lewislou/cell_samples #license-apache-2.0 #endpoints_compatible #has_space #region-us 
# Model Card for cell-seg-sribd



This repository provides the solution of team Sribd-med for NeurIPS-CellSeg Challenge. The details of our method are described in our paper [Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images]. Some parts of the codes are from the baseline codes of the NeurIPS-CellSeg-Baseline repository,

You can reproduce our method as follows step by step:### How to Get Started with the Model

Install requirements by python -m pip install -r URL## Training Details### Training Data

The competition training and tuning data can be downloaded from URL Besides, you can download three publiced data from the following link: Cellpose: URL  Omnipose: URL Sartorius: URL## Environments and Requirements:
Install requirements by### How to use

Here is how to use this model:



If any part of this code is used, please acknowledge it appropriately and cite the paper: