Metadata-Version: 2.1
Name: cellpose-plus
Version: 0.0.11
Summary: cell segmentation framework based on cellpose
Home-page: https://github.com/ITMO-MMRM-lab/cellpose
Author: Israel Huaman
Author-email: ihuaman@itmo.ru
License: BSD
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
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License-File: LICENSE
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# <p>  <b>Cellpose plus</b> </p>

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Cellpose plus is a morphological analysis tool that builds on a forked branch of the state-of-the-art image segmentation framework [Cellpose](https://github.com/MouseLand/cellpose). 
We add feature extraction algorithms to asses morphological properties of cells and nuclei. Achieving a single workflow to study stained cells, from raw images to labeled masks with their corresponding measures. 

Developed by the InfoChemistry scientific center, part of ITMO university.

### Metrics

* Area of subject (𝜇𝑚2).
* Roundness (0.0 - 1.0), having 1.0 for a perfect circle.
* Size ratio between each pair of cell and nucleus.
* Fraction of image covered by cells/nuclei.
* Relative center coordinates.
* Voronoi diagram.
* Voronoi entropy.
* Continuous symmetry measure (CSM). 

### Workflow

<!-- ![Cellpose Plus](repo/workflow.png) -->
<img src="repo/workflow.png" width="800" />

In order to obtain metrics from segmented cells, the initial stained images are merged into a
single image and organized into sub folders to be processed. A cell segmentation
procedure is performed using [Cellpose](https://github.com/MouseLand/cellpose), then we extract metrics 
and finally we store results in the form of images and CSV files.


### Try out online!

You can run Cellpose plus in google colab with a GPU: 
* We provide a commented code-based example notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/ITMO-MMRM-lab/cellpose/blob/main/repo/Cellpose_plus_online.ipynb) showing each part of our workflow.


### Installation

We suggest installing our fork using conda and pip (with >=python3.8).

1. Install [Anaconda](https://www.anaconda.com/download/).
2. Open an anaconda prompt / command prompt which has conda for python 3 in the path.
3. For a new environment for CPU only, run:\
 `conda create -n cellpose_plus 'python==3.9' pytorch` \
 For NVIDIA GPUs, add these additional arguments:\
 `torchvision pytorch-cuda=11.8 -c pytorch -c nvidia`\
4. To activate the new environment, run `conda activate cellpose_plus`
5. To install the latest PyPi release of Cellpose plus, run:\
  `pip install cellpose-plus[gui]`\
  or `pip install cellpose-plus` for a version without GUI.
### How to use

Maybe to attach a link to the manual we wrote, if possible

<img src="repo/demo_gif.gif" width="800" />
<!-- <img src="repo/cellpose_gui.png" width="800" /> -->

### Citation

Here to add the DOI or direct link of the paper

As we work over Cellpose, we ask you to also cite the Cellpose [paper](https://t.co/kBMXmPp3Yn?amp=1).
