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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 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:

Install requirements by

python -m pip install -r requirements.txt

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 

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:

python classification/unsup_classification.py

The results can be stored in data/classification_results/

Using the classified images in data/classification_results/. A resnet18 is trained:

python classification/train_classification.py

Pre-training convnext-stardist using all the images (data/allimages).

python train_convnext_stardist.py

For class 0,2,3 finetune on the classified data (Take class1 as a example):

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.

python train_convnext_hover.py data_dir='data/classification_results/class3'

Finally, four segmentation models will be trained.

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

Docker environment:

docker push lewislou/sribd-cellseg:tagname

The inference process includes classification and segmentation.

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

Calculate the F-score for evaluation:

python compute_metric.py --gt_path path_to_labels --seg_path output_path

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

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.

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/)