About the 2D EM segmentation challenge

Smiley faceExample of ssTEM image and its corresponding segmentation.

Welcome to the first challenge on 2D segmentation of neuronal processes in EM images!

The challenge was launched in the context of the ISBI 2012 conference (Barcelona, Spain, 2-5th May 2012) and remains open to new contributions. If you wish to participate, please register now to be able to download the training and test data sets and upload your own results.

NEW: the paper with all the details about the challenge is finally out!

Background and set-up

In this challenge, a full stack of EM slices will be used to train machine learning algorithms for the purpose of automatic segmentation of neural structures. The images are representative of actual images in the real-world, containing some noise and small image alignment errors. None of these problems led to any difficulties in the manual labeling of each element in the image stack by an expert human neuroanatomist. The aim of the challenge is to compare and rank the different competing methods based on their pixel and object classification accuracy.

How to participate

Everybody can participate in the challenge. The only requirement consists of filling up the registration form to get a user name and password to download the data and upload the results.

This challenge was part of a workshop previous to the IEEE International Symposium on Biomedical Imaging (ISBI) 2012. After the publication of the evaluation ranking, teams were invited to submit an abstract. During the workshop, participants had the opportunity to present their methods and the results were discussed.

Each team received statistics regarding their results. After the workshop, an overview article was compiled by the organizers of the challenge, with up to three members per participating team as co-authors.

If you have any doubt regarding the challenge, please, do not hesitate to contact the organizers. There is also an open discussion group that you can join here.

Training data

Training dataInput training data and corresponding labels.

The training data is a set of 30 sections from a serial section Transmission Electron Microscopy (ssTEM) data set of the Drosophila first instar larva ventral nerve cord (VNC). The microcube measures 2 x 2 x 1.5 microns approx., with a resolution of 4x4x50 nm/pixel.

The corresponding binary labels are provided in an in-out fashion, i.e. white for the pixels of segmented objects and black for the rest of pixels (which correspond mostly to membranes).

To get the training data, please, register, log in and go to the "Downloads" section.

This is the only data that participants are allowed to use to train their algorithms.

Test data

The contesting segmentation methods will be ranked by their performance on a test dataset, also available under "downloads", after registration. This test data is another volume from the same Drosophila first instar larva VNC as the training dataset.

Results format

The results are expected to be submitted as a 32-bit TIFF 3D image, which values between 0 (100% membrane certainty) and 1 (100% non-membrane certainty).

Relevant dates (ISBI 2012 workshop)

  • Deadline for submitting results: February 1st March 1st, 2012.
  • Notification of the evaluation: February 21st March 2nd, 2012.
  • Deadline for submitting abstracts: March 1st March 9th, 2012.
  • Notification of acceptance/presentation type: March 15th March 16th, 2012.

The workshop competition is done but the challenge remains open for new contributions.

References

If you need to cite the challenge, please do so by citing the main publication about the competition:

  • Ignacio Arganda-Carreras, Srinivas C. Turaga, Daniel R. Berger, Dan Ciresan, Alessandro Giusti, Luca M. Gambardella, Jürgen Schmidhuber, Dmtry Laptev, Sarversh Dwivedi, Joachim M. Buhmann, Ting Liu, Mojtaba Seyedhosseini, Tolga Tasdizen, Lee Kamentsky, Radim Burget, Vaclav Uher, Xiao Tan, Chanming Sun, Tuan D. Pham, Eran Bas, Mustafa G. Uzunbas, Albert Cardona, Johannes Schindelin, and H. Sebastian Seung. Crowdsourcing the creation of image segmentation algorithms for connectomics. Frontiers in Neuroanatomy, vol. 9, no. 142, 2015.

Publications about the data: