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Cellsparse API

A web API for Cellsparse implemented with FastAPI.

qupath-extension-cellsparse implements a client for QuPath.

This is a part of the following paper. Please cite it when you use this project.

Install

Mac OSX (Apple silicon or AMD GPUs)

Please check that your system satisfies the requirements listed in the official instructions.
Othrewise, please follow the installation steps for the CPU version.

conda create -n cellsparse-api -y python=3.11
conda activate cellsparse-api
python -m pip install -U pip
python -m pip install "cellsparse-api[tensorflow-macos] @ git+https://github.com/ksugar/cellsparse-api.git"

Windows Native with CUDA-compatible GPU

Microsoft Visual C++ 14.0 or greater is required.
Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/

conda create -n cellsparse-api -y python=3.10
conda activate cellsparse-api
python -m pip install -U pip
conda install -y -c conda-forge cudatoolkit=11.3 cudnn=8.1.0
python -m pip install "tensorflow<2.11"
python -m pip install git+https://github.com/ksugar/stardist-sparse.git
set PYTHONUTF8=1
python -m pip install git+https://github.com/ksugar/cellsparse-api.git
set PYTHONUTF8=0
python -m pip uninstall -y torch torchvision
python -m pip install --no-deps torch torchvision --index-url https://download.pytorch.org/whl/cu113

Windows Native, Mac OSX, Linux, WSL2 (CPU)

Please note that training with CPU is very slow.

On Windows Native, Microsoft Visual C++ 14.0 or greater is required.
Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/

conda create -n cellsparse-api -y python=3.11
conda activate cellsparse-api
python -m pip install -U pip
python -m pip install "cellsparse-api[tensorflow] @ git+https://github.com/ksugar/cellsparse-api.git"

Linux or WSL2 with CUDA-compatible GPU

conda create -n cellsparse-api -y python=3.11
conda activate cellsparse-api
python -m pip install -U pip
conda install -y -c conda-forge cudatoolkit=11.8
python -m pip install "cellsparse-api[tensorflow] @ git+https://github.com/ksugar/cellsparse-api.git"

The following steps are required only if you're using Linux or WSL2 with CUDA-compatible GPU.
If it is not the case, you can move to the Usage section.

Update LD_LIBRARY_PATH

mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' > $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
echo 'export OLD_LD_LIBRARY_PATH=$LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
echo 'export LD_LIBRARY_PATH=$CONDA_PREFIX/lib/:$CUDNN_PATH/lib:$LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
mkdir -p $CONDA_PREFIX/etc/conda/deactivate.d
echo 'export LD_LIBRARY_PATH=${OLD_LD_LIBRARY_PATH}' > $CONDA_PREFIX/etc/conda/deactivate.d/env_vars.sh
echo 'unset OLD_LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/deactivate.d/env_vars.sh
echo 'unset CUDNN_PATH' >> $CONDA_PREFIX/etc/conda/deactivate.d/env_vars.sh
export OLD_LD_LIBRARY_PATH=$LD_LIBRARY_PATH

If you are using WSL2, LD_LIBRARY_PATH will need to be updated as follows.

export LD_LIBRARY_PATH=/usr/lib/wsl/lib:$LD_LIBRARY_PATH

Update nvidia-cudnn-cu11

python -m pip install --no-deps nvidia-cudnn-cu11==8.6.0.163

Solve an issue with libdevice

See details here.

mkdir -p $CONDA_PREFIX/lib/nvvm/libdevice
cp $CONDA_PREFIX/lib/libdevice.10.bc $CONDA_PREFIX/lib/nvvm/libdevice/
echo 'export XLA_FLAGS=--xla_gpu_cuda_data_dir=$CONDA_PREFIX/lib' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
echo 'unset XLA_FLAGS' >> $CONDA_PREFIX/etc/conda/deactivate.d/env_vars.sh
conda install -y -c nvidia cuda-nvcc=11.8

deactivate and activate the environment

conda deactivate
conda activate cellsparse-api

Usage

Launch a server

uvicorn cellsparse_api.main:app

The command above will launch a server at http://localhost:8000.

INFO:     Started server process [21258]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)

For more information, see uvicorn documentation.

Request body

class CellsparseBody(BaseModel):
    modelname: str
    b64img: str
    b64lbl: Optional[str] = None
    train: bool = False
    eval: bool = False
    epochs: int = 1
    trainpatch: int = 224
    batchsize: int = 8
    steps: int = 200
    lr: float = 0.001
    minarea: float = 10.0
    simplify_tol: float = None
key value
modelname Name of a model for training or inference
b64img Base64-encoded image data
b64lbl Base64-encoded label data, required for training
train Specify if the request is for training
eval Specify if the request is for eval/inference
epochs Training epochs
trainpatch Training patch size, Cellpose does not support this parameter
batchsize Training batch size
steps Training steps per epoch
lr Training learning rate
minarea Objects smaller than this value are removed in post processing
simplify_tol A parameter to specify how much simplify the output polygons, no simplification happens if None

Response body

The response body contains a list of GeoJSON Feature objects.

Supporting other formats is a future work.

Citation

Please cite my paper on bioRxiv.

@article {Sugawara2023.06.13.544786,
	author = {Ko Sugawara},
	title = {Training deep learning models for cell image segmentation with sparse annotations},
	elocation-id = {2023.06.13.544786},
	year = {2023},
	doi = {10.1101/2023.06.13.544786},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {Deep learning is becoming more prominent in cell image analysis. However, collecting the annotated data required to train efficient deep-learning models remains a major obstacle. I demonstrate that functional performance can be achieved even with sparsely annotated data. Furthermore, I show that the selection of sparse cell annotations significantly impacts performance. I modified Cellpose and StarDist to enable training with sparsely annotated data and evaluated them in conjunction with ELEPHANT, a cell tracking algorithm that internally uses U-Net based cell segmentation. These results illustrate that sparse annotation is a generally effective strategy in deep learning-based cell image segmentation. Finally, I demonstrate that with the help of the Segment Anything Model (SAM), it is feasible to build an effective deep learning model of cell image segmentation from scratch just in a few minutes.Competing Interest StatementKS is employed part-time by LPIXEL Inc.},
	URL = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.13.544786},
	eprint = {https://www.biorxiv.org/content/early/2023/06/13/2023.06.13.544786.full.pdf},
	journal = {bioRxiv}
}