cellseg-sribd

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The code implementation for cell segmentation

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# Solution of SRIBD-Med Team for NeurIPS2022-CellSeg Challenge

BEFORE YOU RAISE AN ISSUE, PLEASE SEND YOUR QUESTIONS TO lhaof\@sribd.cn AND weilou\@link.cuhk.edu.cn

Institution: Shenzhen Research Institute of Big Data (SRIBD, http://www.sribd.cn/)  
Authors: Wei Lou\*, Xinyi Yu\*, Chenyu Liu\*, Xiang Wan, Guanbin Li, Siqi Liu, Haofeng Li\# (http://haofengli.net/)

This repository provides the solution of team Sribd-med for [NeurIPS-CellSeg](https://neurips22-cellseg.grand-challenge.org/) 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](https://github.com/JunMa11/NeurIPS-CellSeg) repository,

You can reproduce our method as follows step by step:

## Environments and Requirements:
Install requirements by

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

## Dataset
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

## Automatic cell classification
You can classify the cells into four classes in this step.
Put all the images (competition + Cellpose + Omnipose + Sartorius) in one folder (data/allimages).
Run classification code:

```shell
python classification/unsup_classification.py
```
The results can be stored in data/classification_results/

## CNN-base classification model training
Using the classified images in data/classification_results/. A resnet18 is trained:
```shell
python classification/train_classification.py
```
## Segmentation Training
Pre-training convnext-stardist using all the images (data/allimages).
```shell
python train_convnext_stardist.py
```
For class 0,2,3 finetune on the classified data (Take class1 as a example):
```shell
python finetune_convnext_stardist.py model_dir=(The pretrained convnext-stardist model) data_dir='data/classification_results/class1'
```
For class 1 train the convnext-hover from scratch using classified class 1 data.
```shell
python train_convnext_hover.py data_dir='data/classification_results/class3'
```

Finally, four segmentation models will be trained.

## Trained models
The models can be downloaded from this link:
https://drive.google.com/drive/folders/1MkEOpgmdkg5Yqw6Ng5PoOhtmo9xPPwIj?usp=sharing

Docker environment:
```shell
docker push lewislou/sribd-cellseg:tagname
```

## Inference
The inference process includes classification and segmentation.
```shell
python predict.py -i input_path -o output_path --model_path './models' 
```
Colab codes for model inference:
https://colab.research.google.com/drive/1Dk6V6vm0IqaIevjAyjUTuR1nZfT6EvCh?usp=sharing

## Evaluation
Calculate the F-score for evaluation:
```shell
python compute_metric.py --gt_path path_to_labels --seg_path output_path
```
## Finetue on a new dataset
We provide a jupyter notebook to train our model on a new dataset - cellpose step by step.
The notebook codes are in the folder fintune_on_newdataset/finetune.py

## Results
The tuning set F1 score of our method is 0.8795. The rank running time of our method on all the 101 cases in the tuning set is zero in our local
workstation. 
## Acknowledgement
We thank the contributors of public datasets and the organizers of the competition. We thank for the support from the Shenzhen Research Institute of Big Data (SRIBD, http://www.sribd.cn/)

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