Warning

PlantSeg was renamed to PanSeg

To highlight our capablilites beyond plant tissue segmentation, we changed our name to PanSeg!

Please head over to our new repository All future development will continue over there, this repo stays up as an archive.

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

Checkout the documentation 📖 for more details.

full_workflow_20fps.mp4

Installation

The easiest way to get PlantSeg is using the installer. Download it here

The installer comes with python and conda. Please go to the documentation for more detailed instructions.

For development, we recommend to clone the repo and install using:

conda env create -f environment-dev.yaml

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

Repository Index

The PlantSeg repository is organised as follows:

• plantseg: Contains the source code of PlantSeg.
• docs: Contains the documentation of PlantSeg.
• examples: Contains the files required to test PlantSeg.
• tests: Contains automated tests that ensures the PlantSeg functionality are not compromised during an update.
• evaluation: Contains all script required to reproduce the quantitative evaluation in Wolny et al..
• conda-reicpe: Contains all necessary code and configuration to create the anaconda package.
• constructor: Contains scripts for the installer creation.
• Menu: Contains scripts for OS Menu entries

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}
}