Cellseg Sribd

🚀 Cell Segmentation Model (cell-seg-sribd)

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

🚀 Quick Start

You can reproduce our method step by step. First, install the requirements:

python -m pip install -r requirements.txt

✨ Features

• Cell Segmentation: Specifically designed for cell segmentation tasks.
• Multi - modality Images: Capable of handling multi - modality images with low - level cues.

📦 Installation

Install the necessary requirements by running the following command:

python -m pip install -r requirements.txt

💻 Usage Examples

Basic Usage

Here is a basic example of how to use this model:

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))

📚 Documentation

Training Details

Training Data

The competition training and tuning data can be downloaded from [https://neurips22 - cellseg.grand - challenge.org/dataset/](https://neurips22 - cellseg.grand - challenge.org/dataset/). Additionally, you can download three public datasets from the following links:

• Cellpose: https://www.cellpose.org/dataset
• Omnipose: http://www.cellpose.org/dataset_omnipose
• Sartorius: [https://www.kaggle.com/competitions/sartorius - cell - instance - segmentation/overview](https://www.kaggle.com/competitions/sartorius - cell - instance - segmentation/overview)

🔧 Technical Details

The model is a multi - stream cell segmentation model designed for multi - modality images. It utilizes low - level cues to achieve better segmentation results.

📄 License

This project is licensed under the Apache - 2.0 license.

📄 Citation

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

@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}
}

📋 Information Table