Automated 3D cell detection and registration of whole-brain images
Whole-brain cell detection, registration and analysis.
Cellfinder is a collection of tools from the Margrie Lab and others at the Sainsbury Wellcome Centre for the analysis of whole-brain imaging data such as serial-section imaging and lightsheet imaging in cleared tissue.
The aim is to provide a single solution for:
- Cell detection (initial cell candidate detection and refinement using deep learning).
- Atlas registration (using brainreg)
- Analysis of cell positions in a common space
Installation is with
pip install cellfinder.
cellfinder -s signal_images -b background_images -o output_dir --metadata metadata
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, gitter or by raising an issue.
cellfinder takes a stitched, but otherwise raw whole-brain 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
Registration and segmentation (brainreg)
Using brainreg, cellfinder aligns a template brain and atlas annotations (e.g. the Allen Reference Atlas, ARA) to the sample allowing detected cells to be assigned a brain region.
This transformation can be inverted, allowing detected cells to be transformed to a standard anatomical space.
ARA overlaid on sample image
Analysis of cell positions in a common anatomical space
Registration to a template allows for powerful group-level analysis of cellular disributions. (Example to come)
(more to come)
Tracing of inputs to retrosplenial cortex (RSP)
Input cell somas detected by cellfinder, aligned to the Allen Reference Atlas, and visualised in brainrender along with RSP.
Data courtesy of Sepiedeh Keshavarzi and Chryssanthi Tsitoura. Details here
If you find cellfinder 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. and Margrie, T. W. (2020) “A deep learning algorithm for 3D cell detection in whole mouse brain image datasets’ bioRxiv, doi.org/10.1101/2020.10.21.348771
If you use any of the image registration functions in cellfinder, please also cite brainreg.
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.
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