• Version:

    • 0.0.18
  • Release date:

    • 29 June 2021
  • First released:

    • 25 January 2021
  • Development status:

    • 4 - Beta
  • License:

    • BSD-3-Clause

Github Activity

  • Stars: 6
  • Forks: 1
  • Issues + PRs: 11
  • Python versions supported:

    • >=3.7
  • Operating system:

    • OS Independent
  • Requirements:

    • napari
    • napari-plugin-engine (>=0.1.4)
    • napari-ndtiffs
    • brainglobe-napari-io
    • cellfinder-core (>=0.2.4)

cellfinder-napari

Efficient cell detection in large images

License PyPI Python Version tests codecov Downloads Wheel Development Status Code style: black Contributions Website Twitter

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

This package implements the cell detection algorithm from Tyson, Rousseau & Niedworok et al. (2021) for napari, based on the cellfinder-core package.

This algorithm can also be used within the original cellfinder software for whole-brain microscopy analysis.


raw

Visualising detected cells in the cellfinder napari plugin


Instructions

Installation

Once you have installed napari. You can install napari either through the napari plugin installation tool, or directly from PyPI with:

pip install cellfinder-napari

Usage

Full documentation can be found here.

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 by email, on the forum or by raising an issue.


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 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 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 Cassified cell candidates. Yellow - cells, Blue - artefacts

Citing cellfinder

If you find this plugin useful, and use it in your research, please cite the preprint 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

If you use this, or any other tools in the brainglobe suite, please let us know, and we'd be happy to promote your paper/talk etc.

  • Version:

    • 0.0.18
  • Release date:

    • 29 June 2021
  • First released:

    • 25 January 2021
  • Development status:

    • 4 - Beta
  • License:

    • BSD-3-Clause

Github Activity

  • Stars: 6
  • Forks: 1
  • Issues + PRs: 11
  • Python versions supported:

    • >=3.7
  • Operating system:

    • OS Independent
  • Requirements:

    • napari
    • napari-plugin-engine (>=0.1.4)
    • napari-ndtiffs
    • brainglobe-napari-io
    • cellfinder-core (>=0.2.4)

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