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https://github.com/brainglobe/cellfinder-napari

Efficient cell detection in large images using cellfinder in napari
https://github.com/brainglobe/cellfinder-napari

cell-detection cellfinder keras napari napari-plugin object-detection resnet tensorflow

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Efficient cell detection in large images using cellfinder in napari

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README 

        
# This package has moved



`cellfinder-napari` has merged with it's backend code and is now available as a [single package called `cellfinder`](https://github.com/brainglobe/cellfinder).

We recommend you uninstall `cellfinder-napari` and instead use the functionality provided in the `cellfinder` package.



These changes are part of our [wider restructuring](https://brainglobe.info/blog/version1/version_1_announcement.html) of the BrainGlobe suite of tools and analysis pipelines, which you can [keep up to date with on our blog](https://brainglobe.info/blog/index.html).



---



# cellfinder-napari



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### Efficient cell detection in large images (e.g. whole mouse brain images)



`cellfinder-napari` is a front-end to [cellfinder-core](https://github.com/brainglobe/cellfinder-core) to allow ease of use within the [napari](https://napari.org/index.html) multidimensional image viewer. For more details on this approach, please see [Tyson, Rousseau & Niedworok et al. (2021)](https://doi.org/10.1371/journal.pcbi.1009074). This algorithm can also be used within the original

[cellfinder](https://github.com/brainglobe/cellfinder) software for

whole-brain microscopy analysis.



`cellfinder-napari`, `cellfinder` and `cellfinder-core` were developed by [Charly Rousseau](https://github.com/crousseau) and [Adam Tyson](https://github.com/adamltyson) in the [Margrie Lab](https://www.sainsburywellcome.org/web/groups/margrie-lab), based on previous work by [Christian Niedworok](https://github.com/cniedwor), generously supported by the [Sainsbury Wellcome Centre](https://www.sainsburywellcome.org/web/).



----

![raw](https://raw.githubusercontent.com/brainglobe/cellfinder-napari/master/resources/cellfinder-napari.gif)



**Visualising detected cells in the cellfinder napari plugin**



----

## Instructions



### Installation

Once you have [installed napari](https://napari.org/index.html#installation).

You can install napari either through the napari plugin installation tool, or

directly from PyPI with:

```bash

pip install cellfinder-napari

```



### Usage

Full documentation can be

found [here](https://brainglobe.info/documentation/cellfinder/index.html).



This software is at a very early stage, and was written with our data in mind.

Over time we hope to support other data types/formats. If you have any

questions or issues, please get in touch [on the forum](https://forum.image.sc/tag/brainglobe) or by

[raising an issue](https://github.com/brainglobe/cellfinder-napari/issues).



---

## Illustration



### Introduction

cellfinder takes a stitched, but otherwise raw dataset with at least

two channels:

 * Background channel (i.e. autofluorescence)

 * Signal channel, the one with the cells to be detected:



![raw](https://raw.githubusercontent.com/brainglobe/cellfinder/master/resources/raw.png)

**Raw coronal serial two-photon mouse brain image showing labelled cells**



### Cell candidate detection

Classical image analysis (e.g. filters, thresholding) is used to find

cell-like objects (with false positives):



![raw](https://raw.githubusercontent.com/brainglobe/cellfinder/master/resources/detect.png)

**Candidate cells (including many artefacts)**



### Cell candidate classification

A deep-learning network (ResNet) is used to classify cell candidates as true

cells or artefacts:



![raw](https://raw.githubusercontent.com/brainglobe/cellfinder/master/resources/classify.png)

**Cassified cell candidates. Yellow - cells, Blue - artefacts**



## Contributing

Contributions to cellfinder-napari are more than welcome. Please see the [developers guide](https://brainglobe.info/developers/index.html).



## Citing cellfinder



If you find this plugin useful, and use it in your research, please cite the paper outlining the cell detection algorithm:

> Tyson, A. L., Rousseau, C. V., Niedworok, C. J., Keshavarzi, S., Tsitoura, C., Cossell, L., Strom, M. and Margrie, T. W. (2021) “A deep learning algorithm for 3D cell detection in whole mouse brain image datasets’ PLOS Computational Biology, 17(5), e1009074

[https://doi.org/10.1371/journal.pcbi.1009074](https://doi.org/10.1371/journal.pcbi.1009074)



**If you use this, or any other tools in the brainglobe suite, please

 [let us know](mailto:[email protected]?subject=cellfinder-napari), and

 we'd be happy to promote your paper/talk etc.**