Metadata-Version: 2.1
Name: vollseg
Version: 32.2.2
Summary: Segmentation tool for biological cells of irregular size and shape in 3D and 2D.
Home-page: https://github.com/Kapoorlabs-CAPED/vollseg
Author: Varun Kapoor,Claudia Carabana Garcia,Mari Tolonen,Jakub Sedzinski
Author-email: randomaccessiblekapoor@gmail.com
License: BSD-3-Clause
Project-URL: Bug Tracker, https://github.com/Kapoorlabs-CAPED/vollseg/issues
Project-URL: Documentation, https://github.com/Kapoorlabs-CAPED/vollseg#README.md
Project-URL: Source Code, https://github.com/Kapoorlabs-CAPED/vollseg
Project-URL: User Support, https://github.com/Kapoorlabs-CAPED/vollseg/issues
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering :: Image Processing
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: scipy
Requires-Dist: tifffile
Requires-Dist: matplotlib
Requires-Dist: napari
Requires-Dist: cellpose
Requires-Dist: torch
Requires-Dist: test_tube
Requires-Dist: lightning
Requires-Dist: pynvml
Requires-Dist: poetry
Requires-Dist: stardist
Requires-Dist: segment-anything
Provides-Extra: testing
Requires-Dist: tox; extra == "testing"
Requires-Dist: pytest; extra == "testing"
Requires-Dist: pytest-cov; extra == "testing"

# VollSeg

[![Build Status](https://travis-ci.com/kapoorlab/vollseg.svg?branch=master)](https://travis-ci.com/github/kapoorlab/vollseg)
[![PyPI version](https://img.shields.io/pypi/v/vollseg.svg?maxAge=2591000)](https://pypi.org/project/vollseg/)
[![License](https://img.shields.io/pypi/l/napari-metroid.svg?color=green)](https://github.com/kapoorlab/napari-vollseg/raw/main/LICENSE)
[![Twitter Badge](https://badgen.net/badge/icon/twitter?icon=twitter&label)](https://twitter.com/entracod)
![Segmentation](https://github.com/kapoorlab/VollSeg/blob/main/images/Seg_compare-big.png)

VollSeg is more than just a single segmentation algorithm; it is a meticulously designed modular segmentation tool tailored to diverse model organisms and imaging methods. While a U-Net might suffice for certain image samples, others might benefit from utilizing StarDist, and some could require a blend of both, potentially coupled with denoising or region of interest models. The pivotal decision left to make is how to select the most appropriate VollSeg configuration for your dataset, a question we comprehensively address in our [documentation website](https://kapoorlabs-caped.github.io/vollseg-napari/).

## Installation
This package can be installed by 

`pip install  vollseg`

If you are building this from the source, clone the repository and install via

```bash
git clone https://github.com/kapoorlab/vollseg/

cd vollseg

pip install -e .


```

![Algorithm](https://github.com/kapoorlab/VollSeg/blob/main/images/Seg_pipe-git.png)
- **Algorithm**
  - Schematic representation showing the segmentation approach used in VollSeg.
  - First, we input the raw fluorescent image in 3D (A) and preprocess it to remove noise.
  - Next, we obtain the star convex approximation to the cells using Stardist (B) and the U-Net prediction labeled via connected components (C).
  - We then obtain seeds from the centroids of labeled image in B, for each labeled region of C in order to create bounding boxes and centroids.
  - If there is no seed from B in the bounding box region from U-Net, we add the new centroid (in yellow) to the seed pool (D).
  - Finally, we do a marker controlled watershed in 3D using skimage implementation on the probability map shown in (E) to obtain the final cell segmentation result shown in (F).
  - All images are displayed in Napari viewer with 3D display view.
## Requirements

- Python 3.7 and above.


## License

Under MIT license. See [LICENSE](LICENSE).

## Authors

- Varun Kapoor <randomaccessiblekapoor@gmail.com>
- Claudia Carabaña
- Mari Tolonen
