CEM-MitoLab: a dataset of ~22K cellular EM 2D images with label maps of ~135K mitochondrial instances, for deep learning [43720 micrographs in TIFF format]
Publication:
Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model
PLEASE READ! There are 21,860 annotated images in CEM-MitoLab. They contain 135,285 mitochondrial instances in total. The images are a subset of CEM1.5M (also on EMPIAR; EMPIAR-11035). The image patches are mostly 224 x 224 pixels, however some are 512 x 512, and some are smaller. The directory names are either randomized for in-house or unpublished data, or kept as-is for published data. Each directory has "image" and "mask" subdirectory. These directories are populated by grayscale images and "paired" mitochondrial instance label map files, respectively. Both are unsigned byte tiff files. For the mito labels, background is 0, individual instances per image are 1,2,3...255. 2D or 3D = dimensionality of the original EM data LOC-0,1,2 = is the index along z, y, x axis (ie. the numbers following are the location in the original image). Extents of 5, m, n, when present, is because images were present as a flipbook of 5. Only the middle one is annotated and uploaded here with the paired grayscale images. For practical purposes these can be ignored. PLEASE SEE .XLS FILE <> FOR METADATA USING REMBI PRINCIPLES (Sarkans et al Nature Methods 2021)
Files:
Available download options:
Archives and downloads the selected files into an uncompressed zip file.
Depending on the number and size of files to be downloaded, this can take an enormous amount of time.
Also, this feature is unstable and may not work properly, and checksums of downloaded files are not verified.
We recommend using rsync, aspera, globus, etc.
Download a list of selected files.
It is possible to download files by specifying the file list with rsync command, etc.
Archives and downloads the selected files into an uncompressed zip file.
Depending on the number and size of files to be downloaded, this can take an enormous amount of time.
Also, this feature is unstable and may not work properly, and checksums of downloaded files are not verified.
We recommend using rsync, aspera, globus, etc.
Download a list of selected files.
It is possible to download files by specifying the file list with rsync command, etc.