brainglobe listto see which atlases are available.
--save-progressflag added for training. Saves training progress to a .csv file (
--no-save-checkpointsto suppress this behaviour and save disk space. Each model file can be large, and if you don't have much training data, they can be generated quickly.
amap_vishas been replaced with
cellfinder_view_cellshas been replaced with
cellfinder_cell_standardafter refactoring main code are fixed.
cellfinder_view_3Dhas been removed
--soma-diameter) are now defined in um or um3 (rather than mm)
--freeform-use-n-steps) rather than a config file.
cellfinder_view_cellsto deal with Napari API change
cellfinder_gen_region_volcommand line tool to generate images of specific brain regions (for figures etc)
roi_transformnow supports ROIs generated in the original, full-size images
cellfinder_view_cells2Dfunction is removed, and replaced with
roi_transformcommand line tool to transform ImageJ ROI sets into images in standard space.
--max-ramto specify a maximum amount of RAM to use (in GB). Currently only affects processes that will scale to use as much RAM as is available (such as cube extraction).
cellfinder_xml_scalecommand line tool to rescale the cell positions within an XML file. For compatibility with other software, or if your data has been scaled after cell detection.
cellfinder_region_summarynow has a
--sum-regionsflag combine child regions.
cellfinder_region_summaryto be parsed incorrectly is fixed.
base_folderentry is fixed.
urllib3version is specified to prevent atlas downloading from failing.
cells_in_standard_space.xmlfile will be generated. Use
--no-standard_spaceto prevent this behaviour. This functionality can also be run using the standalone script
--metadataflag can be used instead. Supported formats include BakingTray recipe files, mesoSPIM metadata files or cellfinder custom metadata files (see below). If both pixel sizes and metadata are provided, the command line arguments will take priority.
cellfinder_runhas been removed (and may call an old version of the software). Please use
cellfinderfrom now on.
cellfinder_gen_cubeshas been fixed
cellfinder_runwill stop working soon (and may call an old version of the software). Please use
cellfinderfrom now on.
volumes.csvfile which contains the volumes of each brain area in the atlas, in the sample brain.
--summariseflag will now save more information, including:
--figuresflag will create a subdirectory of 3D images in the coordinate space of the downsampled raw data (and registered atlas) that can be used to generate figures e.g. in FIJI. Currently three files will be generated by default.
--remove-intermediateto remove all intermediate files generated by cellfinder. Use with caution.
--y_pixel_mm_network. Currently there is no rescaling in z.
cellfinder_downloadcan be used.
.csv(as well as
.xml), by using
cellfinder_xml_crophas been added. This curates an input
.xmlfile and outputs a file with only those positions within given anatomical areas. Currently, the cells, and the atlas need to be in the same anatomical space.
pip install -e .[dev]in the repository.
--summariseflag. This will run registration (if it wasn't run already) and associate each cell output from the cell detection step with a brain region. This info is saved as a csv file.
--atlas-install-pathflag. This flag can also be used to point to an existing atlas installation (e.g. from aMAP).
cellfinder_runcommand, it won't repeat any parts of the pipeline.
cellfinder_gen_cubesfunction to generate tiff cubes from an xml file independent of the rest of cellfinder. Useful for generating training data.
cellfinder_count_summaryfunction to combine a brain registered to the allen atlas with cell counts. Generates a csv file of cells per region.
cellfinder_region_summaryfunction to organise (align by brain area) csv files of summary cell counts from multiple brains.
cellfinder_view_3Dfunction. A very rudimentary 3D viewer, but can load any file type in use by cellfinder.
--verbosetag was used has been emoved. This was an issue in case cellfinder was stopped in the middle of cell classification as it could cause GPU memory to not be released (and not show up in
--devif you want to build the documentation yourself.
--tensorboardto launch tensorboard automatically during training
cellfinder_viewto launch the (very simple) image/cell viewer