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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: 2 months 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 (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-11-04T22:13:23.000Z (2 months ago)
- Last Synced: 2024-11-04T23:20:06.828Z (2 months ago)
- Topics: bioimage-analysis, bioinformatics, deep-learning, image-segmentation, neural-network, segmentation, unet
- Language: Python
- Homepage: https://kreshuklab.github.io/plant-seg/
- Size: 174 MB
- Stars: 97
- Watchers: 7
- Forks: 31
- Open Issues: 43
-
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
![alt text](docs/logos/logo.png)
[![doc build status](https://github.com/kreshuklab/plant-seg/actions/workflows/build-deploy-book.yml/badge.svg)](https://github.com/kreshuklab/plant-seg/actions/workflows/build-deploy-book.yml)
[![package build status](https://github.com/kreshuklab/plant-seg/actions/workflows/build-deploy-on-conda.yml/badge.svg)](https://github.com/kreshuklab/plant-seg/actions/workflows/build-deploy-on-conda.yml)[![Anaconda-Server Badge](https://anaconda.org/conda-forge/plant-seg/badges/version.svg)](https://anaconda.org/conda-forge/plant-seg)
[![Anaconda-Server Badge](https://anaconda.org/conda-forge/plant-seg/badges/latest_release_date.svg)](https://anaconda.org/conda-forge/plant-seg)
[![Anaconda-Server Badge](https://anaconda.org/conda-forge/plant-seg/badges/downloads.svg)](https://anaconda.org/conda-forge/plant-seg)
[![Anaconda-Server Badge](https://anaconda.org/conda-forge/plant-seg/badges/license.svg)](https://anaconda.org/conda-forge/plant-seg)![Illustration of Pipeline](../assets/images/main_figure_nologo.png)
[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/).
| Documentation | Napari GUI | Legacy GUI | Command Line |
| -------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| [![doc build status](https://img.shields.io/badge/Documentation-Home-blue)](https://kreshuklab.github.io/plant-seg/) | [![doc build status](https://img.shields.io/badge/Documentation-GUI-blue)](https://kreshuklab.github.io/plant-seg/chapters/plantseg_interactive_napari/) | [![doc build status](https://img.shields.io/badge/Documentation-Lecagy-blue)](https://kreshuklab.github.io/plant-seg/chapters/plantseg_classic_gui/) | [![doc build status](https://img.shields.io/badge/Documentation-CLI-blue)](https://kreshuklab.github.io/plant-seg/chapters/plantseg_classic_cli/) |## Installation
Please go to the [documentation](https://kreshuklab.github.io/plant-seg/chapters/getting_started/installation/) 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
```bash
mamba create -n plant-seg -c pytorch -c nvidia -c conda-forge pytorch pytorch-cuda=12.1 pyqt plant-seg --no-channel-priority
```* GPU version, CUDA=11.x
```bash
mamba create -n plant-seg -c pytorch -c nvidia -c conda-forge pytorch pytorch-cuda=11.8 pyqt plant-seg --no-channel-priority
```* CPU version
```bash
mamba create -n plant-seg -c pytorch -c nvidia -c conda-forge pytorch cpuonly pyqt 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.](https://doi.org/10.7554/eLife.57613).
* **examples**: Contains the files required to test PlantSeg.
* **tests**: Contains automated tests that ensures the PlantSeg functionality are not compromised during an update.## 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}
}
```