Allen Mouse CCF v3.1 ASR - Labels Modulo 65000

If you use this dataset, please cite the original Allen Mouse CCF paper (https://doi.org/10.1016/j.cell.2020.04.007):

Quanxin Wang, Song-Lin Ding, Yang Li, Josh Royall, David Feng, Phil Lesnar, Nile Graddis, Maitham Naeemi, Benjamin Facer, Anh Ho, Tim Dolbeare, Brandon Blanchard, Nick Dee, Wayne Wakeman, Karla E. Hirokawa, Aaron Szafer, Susan M. Sunkin, Seung Wook Oh, Amy Bernard, John W. Phillips, Michael Hawrylycz, Christof Koch, Hongkui Zeng, Julie A. Harris, Lydia Ng,The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas,Cell,Volume 181, Issue 4,2020

More information is available in

https://community.brain-map.org/t/allen-mouse-ccf-accessing-and-using-related-data-and-tools/359

This dataset is a 3D mutichannel image which is derived from the original data introduced by the Allen Mouse Common Coordinate Framework v3 (http://help.brain-map.org/download/attachments/2818171/MouseCCF.pdf).

The image file format is a xml/hdf5 file used by ImageJ/Fiji's bigdataviewer (https://docs.openmicroscopy.org/bio-formats/6.1.0/formats/big-data-viewer.html).

All channels are isotropically sampled with a voxel size of 10 micrometers.

Channels:

0: NISSL (http://download.alleninstitute.org/informatics-archive/current-release/mouse_ccf/ara_nissl/ara_nissl_10.nrrd)

1: LABELS Border - Delimits the borders of the labels

2: ARA

3: LABELS Mod 65000 : Labels modulo 65000.

The labels modulo 65000 channel is used because the xml/hdf5 file format handles only 16-bits images, while the reference atlas has (sparse) values above 65535. There is no overlap of labels after computing the label value mod 65000. Thus no loss of information in the mod labels channels.

The related ontology from the allen brain (file 1.json) is also stored here.

The only modification between this version and the previous one is to use an ASR convention for the positioning in 3D. It will match exactly the BrainGlobe allen_mouse atlases.

The xml file which prevents the appearance of a fake extra timepoint in the dataset.