A naive U-Net baseline and submission demo for the microscopy image segmentation challenge in NeurIPS 2022

News:

• The competition proceeding has been published on PMLR https://proceedings.mlr.press/v212/
• Winning algorithms have been publicly available at https://neurips22-cellseg.grand-challenge.org/awards/

Install requirements by

python -m pip install -r requirements.txt

Download training data to the data folder

Preprocess dataset with

python data/pre_process_3class.py

We convert the instance label in ground truth to a three-class label (0-background, 1-interior, 2-edge). After the segmentation, we can convert the three-class label map to an instance map by skimage.measure.label.

Please always keep in mind that this is a instance segmentation task. The baseline is a very simple and naive solution. We highly recommend trying the state-of-the-art methods that mentioned in the end.

See all training options with

python baseline/model_training_3class.py --help

Train baseline model with

python baseline/model_training_3class.py --data_path 'path to training data' --batch_size 8

Run

python predict.py -i input_path -o output_path

Your prediction file should have at least the two arguments: input_path and output_path. The two arguments are important to establishing connections between local folders and docker folders.

Run

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

Cells on the boundaries are not considered during evaluation.

We recommend this great tutorial: https://nbviewer.org/github/ericspod/ContainersForCollaboration/blob/master/ContainersForCollaboration.ipynb

The docker is built based on MONAI

docker pull projectmonai/monai

Prepare Dockerfile

FROM projectmonai/monai:latest

WORKDIR /workspace
COPY ./   /workspace

Put the inference command in the predict.sh

# !/bin/bash -e
python predict.py -i "/workspace/inputs/"  -o "/workspace/outputs/"

The input_path and output_path augments should specify the corresponding docker workspace folders rather than local folders, because we will map the local folders to the docker workspace folders when running the docker container.

The submitted docker will be evaluated by the following command:

docker container run --gpus "device=0" -m 28G --name teamname --rm -v $PWD/CellSeg_Test/:/workspace/inputs/ -v $PWD/teamname_seg/:/workspace/outputs/ teamname:latest /bin/bash -c "sh predict.sh"

• --gpus: specify the available GPU during inference
• -m: spedify the maximum RAM
• --name: container name during running
• --rm: remove the container after running
• -v $PWD/CellSeg_Test/:/workspace/inputs/: map local image data folder to Docker workspace/inputs folder.
• -v $PWD/teamname_seg/:/workspace/outputs/ : map Docker workspace/outputs folder to local folder. The segmentation results will be in $PWD/teamname_outputs
• teamname:latest: docker image name (should be teamname) and its version tag. The version tag should be latest. Please do not use v0, v1... as the version tag
• /bin/bash -c "sh predict.sh": start the prediction command. It will load testing images from workspace/inputs and save the segmentation results to workspace/outputs

Assuming the team name is baseline, the Docker build command is

docker build -t baseline . 

Test the docker to make sure it works. There should be segmentation results in the baseline_seg folder.

docker container run --gpus "device=0" -m 28G --name baseline --rm -v $PWD/TuningSet/:/workspace/inputs/ -v $PWD/baseline_seg/:/workspace/outputs/ baseline:latest /bin/bash -c "sh predict.sh"

During the inference, please monitor the GPU memory consumption using watch nvidia-smi. The GPU memory consumption should be less than 10G. Otherwise, it will run into an OOM error on the official evaluation server.

docker save baseline | gzip -c > baseline.tar.gz

Upload the docker to Google drive (example) or Baidu net disk (example) and send the download link to [email protected].

Please do not upload the Docker to dockerhub!

The naive baseline's primary aim is to give participants out-of-the-box scripts that can generate successful submisions. Thus, there are many ways to surpass this baseline:

• New cell representation methods. In the baseline, we separated touching cells by simply removing their boundaries. More advanced cell representation could be used to address this issue, such as stardist, cellpose, omnipose, deepcell, and so on. We highly recommend trying these SOTA methods.
• New architectures
• More data augmentations and the use of additional public datasets or the set of unlabeled data provided.
• Well-designed training protocols
• Postprocessing

• MONAI APP
• Napari plugin