Edit model card

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 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

python -m pip install -r requirements.txt

How to use

Here is 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))

Citation

If any part of this code is used, 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}
}
Downloads last month
31
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train Lewislou/cellseg_sribd

Space using Lewislou/cellseg_sribd 1