Summary: ThunderSTORM is an open-source, interactive and modular plug-in for ImageJ designed for automated processing, analysis and visualization of data acquired by single-molecule localization microscopy methods such as photo-activated localization microscopy and stochastic optical reconstruction microscopy. ThunderSTORM offers an extensive collection of processing and post-processing methods so that users can easily adapt the process of analysis to their data. ThunderSTORM also offers a set of tools for creation of simulated data and quantitative performance evaluation of localization algorithms using Monte Carlo simulations.Availability and implementation: ThunderSTORM and the online documentation are both freely accessible at https://code.google.com/p/thunder-storm/Contact: guy.hagen@lf1.cuni.czSupplementary information: Supplementary data are available at Bioinformatics online.

With the widespread uptake of 2D and 3D single molecule localization microscopy, a large set of different data analysis packages have been developed to generate super-resolution images. In a large community effort we designed a competition to extensively characterise and rank the performance of 2D and 3D single molecule localization microscopy software packages. We generated realistic simulated datasets for popular imaging modalities-2D, astigmatic 3D, biplane 3D, and double helix 3D-and evaluated 36 participant packages against these data. This provides the first broad assessment of 3D single molecule localization microscopy software and provides a holistic view of how the latest 2D and 3D single molecule localization software perform in realistic conditions. This resource allows researchers to identify optimal analytical software for their experiments, allows 3D SMLM software developers to benchmark new software against current state of the art, and provides insight into the current limits of the field. RESULTS Competition design We established a broad committee from the SMLM community, including experimentalists and software developers, to define the scope of the challenge, ensure realism of the datasets and define analysis metrics. We opened this discussion to all interested parties in an online discussion forum 17. In 2016, we ran a first round of the 3D SMLM competition with explicit submission deadlines, culminating in a special session at the 6th annual Single Molecule Localization Microscopy Symposium (SMLMS 2016). Since then, the challenge has been opened to continuously accept new entries. Thirtysix software packages have been entered in the competition thus far, including four packages used in commercial software (Table S1, Supplementary Note 1). Participation in the competition actually led at least eight teams to modify their software to support additional 3D SMLM modalities, showing how competition can foster microscopy software development. Realistic 3D simulations Testing super-resolution software on experimental data lacks the ground truth information required for rigorous quantification of software performance. Therefore, realistic simulated datasets are required. A critical challenge to in simulating 3D SMLM data was accurate modeling of the

Motivation: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark.Results: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately.Availability and implementation: The challenge Web site (http://www.codesolorzano.com/celltrackingchallenge) provides access to the training and competition datasets, along with the ground truth of the training videos. It also provides access to Windows and Linux executable files of the evaluation software and most of the algorithms that competed in the challenge.Contact: codesolorzano@unav.esSupplementary information: Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

We introduce and demonstrate a new high performance image reconstruction method for super-resolution structured illumination microscopy based on maximum a posteriori probability estimation (MAP-SIM). Imaging performance is demonstrated on a variety of fluorescent samples of different thickness, labeling density and noise levels. The method provides good suppression of out of focus light, improves spatial resolution, and allows reconstruction of both 2D and 3D images of cells even in the case of weak signals. The method can be used to process both optical sectioning and super-resolution structured illumination microscopy data to create high quality super-resolution images.