Single-Molecule Localization Microscopy  •  Software Benchmarking

  • Second edition of the challenge presented at SMLMS 2016

  • Special session on YouTube

  • Super-resolution fight club

  • Publication of the results in Nature Methods 2015

  • Challenge 2013 focussed on 2D, low and high density

  • Super-resolution microscopy wins the 2014 Nobel Prize

  • Why Challenges?

  • Open software initiative for bioimaging informatics

Benchmarking of SMLM Software

Comprehensive review of the single-molecule localization microscopy (SMLM, PALM, STORM) software packages.
This benchmarking uses common reference datasets that simulates biological structures and image formation process. The evaluation of the software packages is performed with well-defined metrics to get an objective and quantitative assessment. The performance test is done be the developers themselves who participated to a challenge 2013 or to the challenge 2016.

Super-resolution microscopy wins the 2014 Nobel Prize in Chemistry

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Super-resolution fluorescence microscopy is an emerging multidisciplinary field that has opened up new opportunities in studying the living cell at the nanometer scale. It overcomes the classical diffraction limit of Abbe, yielding images of biological structures that have lateral resolution down to 10 nm. One of the most promising techniques in this field is single molecule localization. It was announced Method of the Year 2008 by the Nature Methods journal [1] and was was recognized with the 2014 Nobel Prize in chemistry [2]. This technique has been gaining growing attention in microscopy, biology, and engineering conferences, among them ISBI, ever since. The method is based on exciting a spatially sparse set of fluorophores at each time instant, and on fitting the acquired images with a PSF (point spread function) model. This technique was originally proposed and introduced by three independent groups in 2006, giving rise to several acronyms: PALM (photo-activated localization microscopy by Eric Betzig et al.), STORM (stochastic optical reconstruction microscopy by Xiaowei Zhuang et al.), and F-PALM (fluorescence PALM by Samuel Hess). Unlike classical fluorescence microscopy, however, the acquired data cannot be visualized and inspected directly, and an additional processing step is required in this regard. The algorithmic aspects of the single molecule method are therefore extremely important, and this is the focus of the challenge we propose here.

Computational reconstruction by image-analysis software packages

There are currently numerous image-analysis software packages that process super resolution data. A typical data set consists of millions of excited fluorophores, distributed over thousands of images. The excited fluorophores are then detected, localized and visualized by these software tools. Automation is an indispensable aspect of the method [3] and a challenging task [4], as the data is very long image sequence.

Reference

News

Update of the challenge1 Sept. 2017

Thanh-an Pham has a talk at the SMLMS 2017 Symposium in London: Developments of the ongoing 3D SMLM software challenge.

Software 11 Jul. 2017

Update of the directory of SMLM software packages. 83 SMLM software are now identified and referred.

2nd round10 April 2017

From April 10 to May 10 a second round of the challenge is running. All the DH have be submitted with the new normalized datasets.

Participation1 August 2016

A large panel of software have submitted localization files to the challenge in all modalities.

Dataset (3D)6 May 2016

The first dataset is released. The same sample is simulated in 4 modalities: 2D, 3D-astigmatism, 3D-biplane, and 3D-double-helix.

New challenge 24 Aug. 2015

A new challenge will be held in August 2016. Send an email to be kept informed.

Publication 15 June 2015

Advanced online publication of the comparative results on the Nature Methods website.

Online challenge 11 July 2013

The ISBI Challenge is turned to a permanent online challenge.

© 2017 Biomedical Imaging Group, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Last update: 31 Mar 2017