PlantSeg

PlantSeg is a tool for cell instance aware segmentation in densely packed 3D volumetric images. The pipeline uses a two stages segmentation strategy (Neural Network + Segmentation). The pipeline is tuned for plant cell tissue acquired with confocal and light sheet microscopy. Pre-trained models are provided.

Table of Contents

• Getting Started
• Installation
• Repository Index
• Citation

Getting Started

For detailed usage checkout our documentation 📖.

Documentation	Napari GUI	Legacy GUI	Command Line

Installation

Please go to the documentation for more detailed instructions. In short, we recommend using mamba to install PlantSeg, which is currently supported on Linux and Windows.

• GPU version, CUDA=12.x

  mamba create -n plant-seg -c pytorch -c nvidia -c conda-forge pytorch pytorch-cuda=12.1 pyqt plant-seg

• GPU version, CUDA=11.x

  mamba create -n plant-seg -c pytorch -c nvidia -c conda-forge pytorch pytorch-cuda=11.8 pyqt plant-seg

• CPU version

  mamba create -n plant-seg -c pytorch -c nvidia -c conda-forge pytorch cpuonly pyqt plant-seg

The above command will create new conda environment plant-seg together with all required dependencies.

Repository Index

The PlantSeg repository is organised as follows:

• plantseg: Contains the source code of PlantSeg.
• conda-reicpe: Contains all necessary code and configuration to create the anaconda package.
• Documentation-GUI: Contains a more in-depth documentation of PlantSeg functionality.
• evaluation: Contains all script required to reproduce the quantitative evaluation in Wolny et al..
• examples: Contains the files required to test PlantSeg.
• tests: Contains automated tests that ensures the PlantSeg functionality are not compromised during an update.

Citation

@article{wolny2020accurate,
  title={Accurate and versatile 3D segmentation of plant tissues at cellular resolution},
  author={Wolny, Adrian and Cerrone, Lorenzo and Vijayan, Athul and Tofanelli, Rachele and Barro, Amaya Vilches and Louveaux, Marion and Wenzl, Christian and Strauss, S{\"o}ren and Wilson-S{\'a}nchez, David and Lymbouridou, Rena and others},
  journal={Elife},
  volume={9},
  pages={e57613},
  year={2020},
  publisher={eLife Sciences Publications Limited}
}