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NeurIPS-CellSeg

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

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Requirements

Install requirements by

python -m pip install -r requirements.txt

Preprocessing

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.

Training

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

Inference

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.

Compute Evaluation Metric (F1 Score)

Run

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

Cells on the boundaries are not considered during evaluation.

Build Docker

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

1) Preparation

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.

2) Build Docker and make sanity test

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"

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.

3) Save Docker

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 NeurIPS.CellSeg@gmail.com.

Please do not upload the Docker to dockerhub!

Limitations and potential improvements

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:

Extension