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plant-celltype

This repository contains the code for all experiments in the submitted manuscript. The dataset download will be handled automatically by the plant-celltype-graph-benchmark.

Requirements

• Linux
• Anaconda / miniconda

Dependencies

• python >= 3.8
• ctg-benchmark
• tiffile
• scikit-image
• scikit-spatial
• python-elf
• pytorch-lightning

Install dependencies using conda

• for cuda 11.3

conda create -n pct -c rusty1s -c lcerrone -c pytorch -c conda-forge ctg-benchmark cudatoolkit=11.3 tifffile scikit-image scikit-spatial python-elf pytorch-lightning 

• for cuda 10.2

conda create -n pct -c rusty1s -c lcerrone -c pytorch -c conda-forge ctg-benchmark cudatoolkit=10.2 tifffile scikit-image scikit-spatial python-elf pytorch-lightning

• for cpu only

conda create -n pct -c rusty1s -c lcerrone -c pytorch -c conda-forge ctg-benchmark cpuonly tifffile scikit-image scikit-spatial python-elf pytorch-lightning 

Additional dependencies

pip install class_resolver       

Install plantcelltype

With the pct environment active, executed from the root directory:

pip install .

Optional dependencies for visualization

pip install 'napari[pyqt5]'
pip install plotly==5.0.0

Reproduce experiments

All experiments reported in the manuscript are self-contained in experiments, please check the README.md inside the experiment directory for additional instructions.

Process raw data

Features can be computed from segmentation by running:

python run_dataprocessing.py -c example_config/build_dataset/CONFIG-NAME.yaml

Run predictions

To run prediction on new segmentation data using a pretrained model

• Configure the pipeline by editing the prediction config.
• Run the pipeline by:

python run_dataprocessing.py -c example_config/node_predictions/predict_from_segmentation.yaml

Cite

@inproceedings{cerrone2022celltypegraph, title={CellTypeGraph: A New Geometric Computer Vision Benchmark}, author={Cerrone, Lorenzo and Vijayan, Athul and Mody, Tejasvinee and Schneitz, Kay and Hamprecht, Fred A}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={20897--20907}, year={2022} }