Image reconstruction software tools | Super resolution imaging analysis
Super-resolved structured illumination microscopy (SR-SIM) is an important tool for fluorescence microscopy. SR-SIM microscopes perform multiple image acquisitions with varying illumination patterns, and reconstruct them to a super-resolved image. In its most frequent, linear implementation, SR-SIM doubles the spatial resolution. The reconstruction is performed numerically on the acquired wide-field image data, and thus relies on a software implementation of specific SR-SIM image reconstruction algorithms.
Allows users to perform rapid imaging of moving vesicles or loops in the endoplasmic reticulum without motion artifacts. Hessian-SIM is able to reconstruct actin filaments with high fidelity using a low photon budget, which substantially extends the ability to obtain usable super-resolution (SR) images from time-lapse experiments. It permits utilization of sub-millisecond excitation pulses followed by dark recovery times to reduce photobleaching of fluorescent proteins.
An iterative algorithm that converges to the maximum likelihood estimate of the position and intensity of a single fluorophore. An implementation of the algorithm on graphics processing unit hardware achieved more than 10(5) combined fits and Cramer-Rao lower bound calculations per second, enabling real-time data analysis for super-resolution imaging and other applications.
A set of programs to aid in the acquisition and image analysis of data in “photoactivated localization microscopy” (PALM) and “stochastic optical reconstruction microscopy” (STORM). QuickPALM provides a complete solution for acquisition, reconstruction and visualization of 3D PALM or STORM images, achieving resolutions of ~40 nm in real time. This software package should greatly facilitate the conversion of many laser-excitation widefield or TIRF microscopes into powerful super-resolution microscopes.
Provides a complete set of tools for automated processing, analysis and visualization of data acquired by single-molecule localization microscopy (SMLM) methods. ThunderSTORM is a program that offers many different processing and post-processing methods so that users can adapt the analysis to their data. It is able to process the data using any combination of the implemented feature enhancing, spot detection and fitting methods.
Enables super-resolved structured illumination microscopy (SR-SIM) reconstructions for a large range of SR-RIM platform. FairSIM works with all image formats supported by ImageJ. It uses two and three-beam interference for pattern generation to process single-slice reconstructions of SR-SIM systems compatible. This software provides access to several intermediate results of the parameter estimation and reconstruction process in both frequency and spatial domains.
Provides a user-friendly means of visualizing, filtering and analyzing localization microscopy (LM) data. PALMsiever includes drift correction, clustering, intelligent line profiles, many rendering algorithms, and 3D data visualization. It incorporates the main analysis and data processing modalities used by experts in the field, as well as several new features we developed, and makes them broadly accessible. It can easily be extended via plugins and is provided as free of charge open-source software.
An interactive open-source software with a graphical user interface, which allows performing processing steps for localization data in an integrated manner. This includes common features and new tools such as correction of chromatic aberrations, drift correction based on iterative cross-correlation calculations, selection of localization events, reconstruction of 2D and 3D datasets in different representations, estimation of resolution by Fourier ring correlation, clustering analysis based on Voronoi diagrams and Ripley’s functions. SharpViSu is optimized to work with eventlist tables exported from most popular localization software. The functionality of SharpViSu is extendable via plugins, such as ClusterViSu for comprehensive cluster analysis of localization microscopy data. It includes tools such as calculations of Voronoi and Ripley statistics with Monte-Carlo simulations, different modes of reconstruction (e.g. based on Gaussian blur or Ripley’s functions) and segmentation of density maps, retrieval of geometrical properties of detected clusters, segmentation based on Voronoi tessellation.
An open-source, modular set of functions for MATLAB equipped with a user friendly graphical interface and designed for processing 2D and 3D data acquired by structured illumination microscopy. Both optical sectioning and super-resolution applications are supported. The software is also capable of maximum a posteriori probability image estimation (MAP-SIM), an alternative method for reconstruction of structured illumination images. MAP-SIM can potentially reduce reconstruction artifacts, which commonly occur due to refractive index mismatch within the sample and to imperfections in the illumination.
A grid-based clustering algorithm FOCAL, which explicitly accounts for several dominant artifacts arising in SMLM image reconstructions. FOCAL is fast and efficient, scaling like O(n), and only has one set parameter. We assess DBSCAN and FOCAL on experimental dSTORM data of clusters of eukaryotic RNAP II and PALM data of the bacterial protein H-NS, then provide a detailed comparison via simulation. FOCAL performs comparable and often superior to DBSCAN while yielding a significantly faster analysis. Additionally, FOCAL provides a novel method for filtering out of focus clusters from complex SMLM images.
Allows users to analyze single molecule localization microscopy (SMLM) data. SMLocalizer is a ImageJ2 plugin for SMLM image processing, based on a combination of established SMLM algorithms. The software supports 3D SMLM through PRILM, double helix, astigmatism and biplane modalities. Advanced users can manually modify all parameters in the user interface.
Assists users to reconstruct the structure of high-resolution fluorescent microcopy. DLBI is a deep learning guided Bayesian inference framework that combines the strength of stochastic simulation, deep learning and statistical inference. It contains three modules: (i) stochastic simulation, (ii) deep neural networks and (iii) Bayesian inference. This method can correct and refine the ultrastructure learned by deep learning, and thus enhance the physical meaning of the final super-resolution image.
Allows local density estimation, segmentation and quantification of 3D single-molecule localization microscopy (SMLM) data. voronoi3D was validated using simulated and experimental data, illustrating its applicability to different biological objects of interest. It can be used to quantify 3D information such as spatial organization, local density, volume and shape of labelled protein clusters in 3D and number of molecules within a given cluster.
Analyzes and quantifies 3D information of 3D single-molecule localization microscopy (SMLM) data. 3DClusterViSu can obtain spatial organization, local density, volume and shape of labelled protein clusters in 3D and number of molecules within a given cluster. It is applicable to different biological objects of interest such as fine chromatin structures in a dense cellular context. This tool is based on mathematical properties of 3D Voronoi diagrams.
Provides unbiased localization on continuous space and high recall rates for high-density imaging, and to have orders-of-magnitude shorter run times compared to previous high-density algorithms. FALCON was validated on both simulated and experimental data.
Analyzes high-density 2D STORM data using compressed sensing. For an experimental data set with varying emitter density, L1H analysis is ~300-fold faster than interior point methods. This drastic reduction in computational time should allow the compressed sensing approach to be routinely applied to super-resolution image analysis.
Improves the resolution of conventional fluorescent microscopy by one order of magnitude. SimpleSTORM is based on a carefully designed yet simple model of the image acquisition process which allows you to standardize each image such that the background has zero mean and unit variance. This standardization makes it possible to detect spots by a true statistical test (instead of hand-tuned thresholds) and to de-noise the images with an efficient matched filter.
An open source, real-time data analysis and rendering tool for super-resolution imaging techniques that are based on single molecule detection and localization (e.g. stochastic optical reconstruction microscopy - STORM and photoactivation localization microscopy – PALM). GraspJ is an ImageJ plug-in with a convenient user interface, that allows high accuracy localization of single molecules as well as processing and rendering of high resolution images in real-time. GraspJ includes several features such as drift correction, multi-color, 3D analysis/rendering, and is compatible with a large range of data acquisition software. In addition, it allows easy interfacing with other image processing tools available with ImageJ.
Enables online image processing essential for high-throughput nanoscopy. WindSTORM is an online application for high-density emitter localization that uses non-iterative linear deconvolution to decompose overlapping emitters and retrieve their precise locations. It achieves real-time image processing on a GPU device and maintains high accuracy and fidelity even in the presence of high non-uniform background in various biological samples.
Facilitates works involved in super-resolution optical imaging (PALM, STORM etc). By providing an intuitive graphical user interface front end, we hope it can serve as a useful tool for a wide range of scientists, including experimental biologists as well as physicists. The program runs as a plugin of the (extremely versatile) ImageJ software, thus can be used on any image format that is supported by ImageJ.
Assists in reconstructing dense localization microscopy datasets. B-recs is an application that builds back super-resolution images at intermediate densities of active fluorophores. The theoretical limit for accurate reconstruction is one fluorophore per pixel on the camera, at higher densities, the accuracy quickly drops. Two options are offered to run this method: fiji plugin and command line tool.