Awesome

PlantSeg <!-- omit in toc -->

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 <!-- omit in toc -->

• Getting Started
• Installation
• Repository Index
• Citation

Getting Started

For detailed usage checkout our documentation 📖.

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 (replace 12.1 with 11.8 for CUDA=11.x)

  mamba create -n plant-seg -c pytorch -c nvidia -c conda-forge pytorch pytorch-cuda=12.1 plant-seg --no-channel-priority
  

• CPU version

  mamba create -n plant-seg -c pytorch -c nvidia -c conda-forge pytorch cpuonly plant-seg --no-channel-priority
  

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.
• docs: Contains the documentation of PlantSeg.
• 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}
}