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NeurIPS-CellSeg

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Naive baseline for microscopy image segmentation challenge in NeurIPS 2022

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# NeurIPS-CellSeg

A naive U-Net baseline and submission demo for the [microscopy image segmentation challenge in NeurIPS 2022](https://neurips22-cellseg.grand-challenge.org/)

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/

## Requirements

Install requirements by

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

## Preprocessing

Download training data to the `data` folder

Preprocess dataset with

```shell
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

```shell
python baseline/model_training_3class.py --help
```

Train baseline model with

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

## Inference

Run

```shell
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

```shell
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](https://hub.docker.com/r/projectmonai/monai)

```shell
docker pull projectmonai/monai
```

Prepare `Dockerfile`

```dockerfile
FROM projectmonai/monai:latest

WORKDIR /workspace
COPY ./   /workspace
```

Put the inference command in the `predict.sh`

```bash
# !/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:

```bash
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

```shell
docker build -t baseline . 
```

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

```bash
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

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

Upload the docker to Google drive ([example](https://drive.google.com/file/d/1CQRP6yvv9le7m8k7PI_CR6iZAo4vVTt8/view?usp=sharing)) 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:

- 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](https://github.com/stardist/stardist), [cellpose](https://github.com/MouseLand/cellpose), [omnipose](https://github.com/kevinjohncutler/omnipose), [deepcell](https://github.com/vanvalenlab/deepcell-tf), and so on. **We highly recommend trying these SOTA methods.**
- New architectures
- More data augmentations and the use of additional [public datasets](https://grand-challenge.org/forums/forum/weakly-supervised-cell-segmentation-in-multi-modality-microscopy-673/topic/official-external-datasets-thread-720/) or the set of unlabeled data provided.
- Well-designed training protocols
- Postprocessing

## Extension

- [MONAI APP](https://git.linhan.ml/linhandev/monai-app)
- [Napari plugin](https://github.com/YaoZhang93/napari-cellseg)

NeurIPS-CellSeg

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