https://github.com/kreshuklab/plant-seg
A tool for cell instance aware segmentation in densely packed 3D volumetric images
https://github.com/kreshuklab/plant-seg
bioimage-analysis bioinformatics deep-learning image-segmentation neural-network segmentation unet
Last synced: 9 days ago
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A tool for cell instance aware segmentation in densely packed 3D volumetric images
- Host: GitHub
- URL: https://github.com/kreshuklab/plant-seg
- Owner: kreshuklab
- License: mit
- Created: 2019-10-18T10:56:08.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2025-04-29T15:50:56.000Z (16 days ago)
- Last Synced: 2025-04-29T16:36:36.403Z (16 days ago)
- Topics: bioimage-analysis, bioinformatics, deep-learning, image-segmentation, neural-network, segmentation, unet
- Language: Python
- Homepage: https://kreshuklab.github.io/plant-seg/
- Size: 176 MB
- Stars: 111
- Watchers: 7
- Forks: 35
- Open Issues: 41
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-biological-image-analysis - PlantSeg - Tool for cell instance aware segmentation in densely packed 3D volumetric images. (Plant science)
README
# PlantSeg
[](https://github.com/kreshuklab/plant-seg/actions/workflows/build-and-test-package.yml)
[](https://github.com/kreshuklab/plant-seg/actions/workflows/build-and-publish-docs.yml)
[](https://anaconda.org/conda-forge/plant-seg)
[](https://anaconda.org/conda-forge/plant-seg)
[](https://anaconda.org/conda-forge/plant-seg)
[](https://anaconda.org/conda-forge/plant-seg)
[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](#getting-started)
* [Installation](#installation)
* [Repository Index](#repository-index)
* [Citation](#citation)
## Getting Started
For detailed usage checkout our [**documentation** 📖](https://kreshuklab.github.io/plant-seg/).
## Installation
The easiest way to get PlantSeg is using the installer. [**Download it here**](https://heibox.uni-heidelberg.de/d/72b4bd3ba5f14409bfee/)
The installer comes with python and conda.
Please go to the [documentation](https://kreshuklab.github.io/plant-seg/chapters/getting_started/installation/) for more detailed instructions.
For development, we recommend to clone the repo and install using:
```bash
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.](https://doi.org/10.7554/eLife.57613).
* **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
```text
@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}
}
```