cellfinder-napari 0.0.16rc0

Efficient cell detection in large images

cellfinder-napari

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.

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Visualising detected cells in the cellfinder napari plugin

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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.

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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 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):

Candidate cells (including many artefacts)

Cell candidate classification

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

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.