Electron Microscopy (CA1 hippocampus) Dataset

folder ElectronMicroscopy_Hippocampus (5 files)
filetesting.tif 129.92MB
filetesting_groundtruth.tif 129.92MB
filetraining.tif 129.92MB
filetraining_groundtruth.tif 129.92MB
filevolumedata.tif 3.35GB
Type: Dataset

title= {Electron Microscopy (CA1 hippocampus) Dataset},
keywords= {},
author= {},
abstract= {The dataset available for download on this webpage represents a 5x5x5µm section taken from the CA1 hippocampus region of the brain, corresponding to a 1065x2048x1536 volume. The resolution of each voxel is approximately 5x5x5nm. The data is provided as multipage TIF files that can be loaded in Fiji.



We annotated mitochondria in two sub-volumes. Each sub-volume consists of the first 165 slices of the 1065x2048x1536 image stack. The volume used for training our algorithm in the publications mentionned at the bottom of this page is the top part while the bottom part was used for testing.

Although our line of research was primarily motivated by the need to accurately segment mitochondria and synapses, other structures are of interest for neuroscientists such as vesicles or cell boundaries. This dataset was acquired by Graham Knott and Marco Cantoni at EPFL. It is made publicly available in the hope of encouraging similar sharing of useful data amongst researchers and also accelerating neuroscientific research.

For further information, please visit http://cvlab.epfl.ch/research/medical/em/mitochondria.

total 3.7G
124M testing_groundtruth.tif
124M testing.tif
124M training_groundtruth.tif
124M training.tif
3.2G volumedata.tif

### References

A. Lucchi Y. Li and P. Fua, Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets, Conference on Computer Vision and Pattern Recognition, 2013.
A. Lucchi, K.Smith, R. Achanta, G. Knott, P. Fua, Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks with Learned Shape Features, IEEE Transactions on Medical Imaging, Vol. 30, Nr. 11, October 2011.
terms= {},
license= {},
superseded= {},
url= {https://cvlab.epfl.ch/data/em}

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