Electron Microscopy Dataset

Data

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.

Download dataset

 

Download training sub-volume Download testing sub-volume
Download groundtruth training sub-volume Download groundtruth testing sub-volume

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 link.

Results

The following results were produced with the algorithm presented in our CVPR13 publication:
Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets  [pdf].

Download results (binary cube)

References

A. Lucchi; Y. Li; P. Fua : Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets. 2013. Conference on Computer Vision and Pattern Recognition (CVPR), Portland, Oregon, USA, June 23-28, 2013. DOI : 10.1109/Cvpr.2013.259.
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. 2011. DOI : 10.1109/TMI.2011.2171705.