Efficient cell detection in large images
Efficient cell detection in large images (e.g. whole mouse brain images)
This algorithm can also be used within the original cellfinder software for whole-brain microscopy analysis.
Visualising detected cells in the cellfinder napari plugin
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
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.
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
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
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.
- 29 June 2021
- 25 January 2021
- 4 - Beta
- Stars: 6
- Forks: 1
- Issues + PRs: 11
Python versions supported:
- OS Independent
- napari-plugin-engine (>=0.1.4)
- cellfinder-core (>=0.2.4)