Fiji

Today, the ImageJ team is pleased to announce a public release candidate for ImageJ2: version 2.0.0-rc-2. We tested the first release candidate internally, but now is the time to engage the community: please test the second release candidate with ... [More] your favorite workflows. Unlike the beta releases during the past two years, this version of ImageJ2 integrates fully with the legacy ImageJ 1.x user interface. Consequently, this version of ImageJ2 really is the same ImageJ you know and love, achieving 100% backwards compatibility with existing ImageJ 1.x plugins, scripts and macros, while still providing access to the redesigned capabilities of ImageJ2. This allows you to keep using ImageJ in familiar ways, while also enabling migration toward more powerful new features as needed. The Fiji distribution of ImageJ has bundled ImageJ2 for quite some time, so you may already be familiar with some of ImageJ2's features, some of which—such as the Updater and Launcher—were originally developed as part of Fiji, now they are part of ImageJ proper. But this release candidate represents the first time Fiji has really been built fully on top of ImageJ2. Contents 1 Features of ImageJ2 2 Enhanced scripting 3 More than just an application 4 How to test the release candidate? Features of ImageJ2 ImageJ2 provides a wealth of new features and capabilities: The ImageJ Updater makes it simple to keep your ImageJ up to date, and to add new plugins by enabling additional Update Sites. The ImageJ Updater also makes it simple to publish your own tools based on ImageJ, via Personal Update Sites. New and enhanced file format support via the SCIFIO library. There is no need to call a special SCIFIO plugin; it works with commands like File > Open automatically. Additional import options are available via the File > Import > Image... command. See the SCIFIO page of the ImageJ wiki for further details. New commands: Plugins > Debug > Dump Stack for debugging when things hang. Plugins > Debug > System Information for reporting on versions of installed plugins and libraries. More powerful Script Editor with support for several scripting languages through a generic, consistent scripting framework (see screenshot below). Write parameterized commands and scripts: Declare typed inputs and outputs with the @Parameter annotation and let ImageJ handle the user interaction, avoiding any dependence on the AWT user interface (see example below). Parameter support for scripts and macros (see section below) Reusable in many contexts: KNIME, CellProfiler, OMERO, headless... Plugins appear in the menu automatically without plugins.config files, and without having to set the plugins.dir property to a single directory containing all the .jar files with special naming requirements. Mix and match ImageJ 1.x and ImageJ2 data structures! Example: import ij.ImagePlus; import org.scijava.command.Command; import org.scijava.plugin.Parameter; import org.scijava.plugin.Plugin; @Plugin(type = Command.class, menu = "Plugins > Set Image Title") public class SetImageTitle implements Command { @Parameter private ImagePlus imp; @Parameter private String title; @Override public void run() { imp.setTitle(title); } } Easy yet powerful plugin concept—stay tuned for a dedicated blog post soon! Use ImageJ2's N-dimensional ImgLib2-based data structures (still in beta). Enhanced scripting ImageJ2 comes with a versatile and elegant scripting framework: all languages can be accessed in the same, consistent way. New languages can be supported by implementing script language plugins; the script editor will automatically support such languages. By default, ImageJ supports Beanshell, Javascript, Jython, JRuby, Clojure, ImageJ 1.x macro and Java "as a script language". Experimental alpha versions are available for Scala and native Python. Similar to commands supporting @Parameter annotations asking ImageJ to auto-generate dialogs, scripts support a related concept: By declaring typed variables in commented lines on top of the script, ImageJ (or KNIME, CellProfiler, OMERO, etc) will be asked to auto-generate the appropriate dialogs when executing the scripts. Identical to thusly parameterized scripts, it is now also possible to declare in ImageJ 1.x macros what input parameters are required (see the ImageJ2 script editor screenshot on the right with a parameterized macro in action). More than just an application ImageJ2 breaks the "one developer, one machine, one task" paradigm: it is also a modular collection of highly reusable software libraries built on the SciJava software stack, using a powerful plugin framework to facilitate rapid development and painless user customization. The following software component libraries form the core of ImageJ2: ImageJ Common - The core image data model, using ImgLib2. ImageJ OPS - An extensible framework for reusable image processing algorithms. ImageJ Updater - A mechanism to update individual plugins and libraries within ImageJ. ImageJ Legacy - Provides complete backwards compatibility with ImageJ 1.x. SciJava Common - The core frameworks for plugins, modules, scripting and the application itself. How to test the release candidate? You can either download the .zip file or just update your Fiji installation: ImageJ 2.0.0-rc-2 is already part of Fiji. Please send all bug reports to the ImageJ mailing list. And thank you very much for trying ImageJ2! [Less]

We just released a new minor version of TrackMate, that ships improvements to TrackScheme and bugfixes. Improvements TrackScheme now has an outline panel, useful to navigate in large models. TrackScheme has a better layout for tracks that ... [More] have merge and split events. The 'Refresh' button on detector config panels is not present anymore. It's confusing and useless now that the quality value does not match pixel intensity at all. TrackMate has new logos and icons, made by Agnieszka Kawska @ IlluScientia. Bugfixes Fixed several problems with the 'Copy Overlay' action. Fixed a severe TrackScheme bug, happening when creating links with invisible tracks. Fixed a detector crash when running a preview detection with 0 spots found. [Less]

The Fiji/ImageJ2 teams are pretty busy these days to finalize the first non-beta version of ImageJ2. Quite a few things have changed in Fiji itself as a consequence, most of them behind the scenes (our apologies when things break, and our heartfelt ... [More] thanks for all your help to stabilize this project). In this context, as of today, Fiji switches its editor to the ImageJ2 script editor. A lot of effort has gone into improving the Fiji Script Editor in the ImageJ2 context, most of it behind the scenes: there is a consistent, elegant and powerful scripting framework now, forming the basis of all of ImageJ2's scripting capability, including the support for the legacy ImageJ 1.x macro language. The main benefit is that the script editor is now fully plug 'n play: adding the .jar file implementing a new scripting language will automatically add it to the script editor language menu; even custom syntax highlighting can be added that way. The biggest bonus for users, however, is that you can use the @Parameter framework for painless user interaction, even from ImageJ 1.x macros! Example: // @String(label = "What is your first name?") name print("Hello, " + name + "!"); Enjoy! [Less]

We just released a new minor version of TrackMate, pressed by the many bugs that were found and appeared between this version and the previous one. It focuses mainly on interoperability and of course bugfixes. Highlights It is possible to manually ... [More] color spots and edges. The colors attributed are saved and retrieved from XML. To use it, select a few cells in TrackScheme and right-click to make the contextual menu appear: There is now an action to export to Icy track manager. It generates a XML file that can be opened in the Icy track manager. Icy XML track files can be opened in TrackMate. Just point the TrackMate loader plugin to such a file, and its type will be detected and it will be opened in TrackMate. We would like to express our thanks to Fabrice de Chaumont for his help in developing these two features. A new action allows merging two TrackMate files into one. The color scale used to display colored tracks, links and spots can now be manually adjusted. In the display panel of the GUI, double click on the label "color by" and a settings window will open allowing the user to select between manual or automatic scaling. This is great to generate views that are comparable between datasets. This feature is commissioned and offered by Fumio Hayashi, Kobe University. Minor improvements The table in the InfoPane of TrackScheme displays the full spot name. Auto-linking mode allows the creation of links backward in time. Two new track feature analyzers allow filtering tracks by their spots quality or by their longest gap. Bugfixes Fix a potential (and potentially long standing) bug with numerical input on machine using locales different from the US locale. Put back the forgotten Manual Tracker. Save and retrieve the tracker used in the XML file. Fix crash triggered when the specified ROI had points out of the source image bounds. Fix crash when saving with a source image not loaded from disk. Do not crash when saving unconfigured detector or tracker. Many other bugfixes. [Less]

Stephan Preibisch's multiview deconvolution algorithm and the associated Fiji plugin was published at Nature Methods. The approach is relevant especially as a fusion strategy for SPIM data, Stephan Preibisch, Fernando Amat, Evangelia Stamataki ... [More] , Mihail Sarov, Robert H Singer, Eugene Myers & Pavel Tomancak (2014) Efficient Bayesian-based multiview deconvolution Nature Methods AOP doi:10.1038/nmeth.2929 PDF, Supplement Check out the extensive supplement that provides the derivation of the algorithm (for the mathematically inclined) and extensive evaluation and benchmarking against other approaches. The GPU code was developed by Fernando Amat from Philipp Keller's lab at the Janelia Farm. The paper comes with an extensive collection of Supplementary Videos available at Nature Methods website. If you are interested in the paper's genesis under intense but constructive scrutiny of reviewers, you may want to have a look at the various submitted pre-prints on arxiv arxiv v1 arxiv v2 arxiv v3 The documentation for the Fiji plugin contains description of parameters and a 'how to' for hacks that didn't yet make it into the plugins menu's. It complements other SPIM related Fiji plugins such as bead based registration & Multiview fusion. Finally, a Figure from the paper showing that multi-view deconvolution matters! This is probably not the last answer to SPIM data deconvolution. We are looking forward to the input from the computer vision community to this hard problem. [Less]

A new minor version of TrackMate_ has been released. This page lists the changes that are visible to the user. Version 2.2.0 focuses mainly on extendability, tracking performance and detection accuracy. Highlights Major improvement for tracking ... [More] performance: The LAP trackers are now based on the Jonker-Volgenant solver, which performs better than the Munkres-Kuhn we were using until then. Our initial tests report that this new version runs 2x to 4x faster. A major problem was found in the LoG detector: Because of a severe rounding error, the LoG detector was not behaving as a true LoG detector, which strongly hindered its accuracy and potence. The problem is now fixed and the accuracy of the detector has vastly improved. You should now observe much better results when using this detector: less spurious spots, better spot size sensitivity, better sensitivity to faint spots. On its side, the DoG detector now handles spots that lie on the border of the images better. Of course, the results will be different when comparing to v2.1.1. We also took the chance to rewrite all the detectors from scratch, using the latest development in ImgLib2. This prompted major performance improvement for the detection process. Here are the results of a quick benchmark: Detector v2.1.1 v2.2.0 speedup factor LoG detector for 2D images 13.4s 1.2s x11 DoG detector for 2D images 3.5s 1.1s x3 LoG detector for 3D images 291s 18.2s x15 DoG detector for 3D images 21.6s 15.1s x1.4 (This benchmark was done with the ISBI challenge datasets). You can find more benchmarks here. Complete rewrite of the way we handle modules in TrackMate. TrackMate now uses SciJava and exploit its automatic discovery mechanism for modules. Practically, it is now very easy to extend TrackMate, and you do not depend on us anymore at all for anything. We could completely disappear and you would still be able to extend TrackMate so that it suits your need, without requiring any of our help. Several tutorials describe how to do this. This is made possible thanks to the enormous efforts of the SciJava team. Thanks to them! Improvements In TrackScheme, the tracks can be navigated through using the keyboard: UP: previous spot in time. DOWN: next spot in time. LEFT: next sibling within the same track RIGHT: previous sibling within the same track PAGE-DOWN: jump to next track, same frame PAGE-UP: jump to previous track, same frame Major performance improvement when launching TrackScheme on a large models. Major performance improvement when selecting/deselecting in TrackScheme The 3D viewer is now in sync with manual modification. Any edit made to the model is immediately echoed on the 3D viewer. This comes at a price: the 3D viewer cannot be used for very large model in an efficient manner. The user can now define a depth of drawing display setting, that limits the number of Z-slices on which we draw the model. This is useful on very thick samples where the drawing of all the data across all Zs can become confusing. Minor improvement for the track display on the main view, for large models. Ship a MATLAB function to import in MATLAB the tracks exported by TrackMate. Check this. Warn the user if they feed TrackMate with a stack that has no time-points by plenty of Z-slices. This is one of the main reason we get irrelevant bug reports: By default, ImageJ considers that a multi-image file is always made of 1 time-points and many Z-slices. Whereas users mean to feed TrackMate with a movie made of several 2D planes filmed over time. We now warn the user that the dimensionality stored in the image might not be the one they expect. Fine tune multithreading. Before this version, TrackMate was doing multithreading during detection in the following way: We run the detection process on as many frames as we have threads, and each frame is processed using 1 thread. This is suboptimal if we have a large number of threads, but a few number of frames (e.g. 1 frame, 24 threads). This release fixes this: If we have 10 threads and 15 frames to process, we process 10 frames at once, and allocate 1 thread per frame. But if we have 10 threads and 2 frames, we process the 2 frames at once, and allocate 5 threads per frame if we can. For this to work of course, the detector must be multithreaded, which is the case with the ones we ship now. TrackMate can now load partial TrackMate files, and still display all the information it could retrieve, instead of generating an error and quitting. This is useful if you have file that contains the model section, but not the settings section. Improved user messaging when loading a file. Spots can be colored by the features of the track they belong to. Slightly better memory management for spot, edge and track coloring for large models. The GUI does not freeze when computing histograms. Bug fixes Fix a major bug with the DoG detector preventing to use it with a ROI. Fixed a minor warning triggered when using the 3D viewer as the main view for TrackMate. When navigating backward in the GUI, the model is cleared when it is sensible. This is made to avoid having "ghost" results floating over unprocessed images. Fix several issues with zooming and decorations in TrackScheme. Fix a bug that caused the sliders to disappear for the simple style and with some zoom levels. Zooming should be now more efficient, and offers more zoom levels. JeanYvesTinevez (talk) 05:32, 19 March 2014 (CDT) [Less]

The Squassh protocol (Segmentation and Quantification of Sub-cellular Shapes) provides a robust and versatile workflow for detecting, segmenting, and quantifying sub-cellular markers in fluorescence microscopy images and analyzing their ... [More] co-localiztion. The protocol is particularly tuned to the needs of high-content high-throughput assays and directly performs all the necessary statistical significance tests. The Squassh protocol is based on a novel image-segmentaiton theory from the MOSAIC Group, which provably provides globally optimal segmentation results with sub-pixel resolution. As an additional feature, the method accounts for the point-spread function of the microscope, hence producing optimally deconcolved and denoised results. The Squassh workflow critically depends on Fiji as its software processing pipeline. The method, the Fiji software, and its application are described in: A. Rizk, G. Paul, P. Incardona, M. Bugarski, M. Mansouri, A. Niemann, U. Ziegler, P. Berger, and I. F. Sbalzarini. Segmentation and quantification of subcellular structures in fluorescence microscopy images using Squassh. Nature Protocols, 9(3):586–596, 2014. [Less]

At the IEEE Symposium on Biomedical Imaging (ISBI) 2012, a team led by Erik Meijering and Jean-Christophe Olivo-Marin organized a grand challenge competition in particle tracking. 14 popular algorithms and software implementations for object ... [More] tracking and particle tracking in fluorescence microscopy images have been objectively compared on 48 data sets mimicking the motion of viruses, endosomes, single receptors, and microtubule-dependent transport for different object densities and image qualities (SNR). The results have now been published in the open-access article: N. Chenouard, I. Smal, F. de Chaumont, M. Maska, I. F. Sbalzarini, Y. Gong, J. Cardinale, C. Carthel, S. Coraluppi, M. Winter, A. R. Cohen, W. J. Godinez, K. Rohr, Y. Kalaidzidis, L. Liang, J. Duncan, H. Shen, Y. Xu, K. E. G. Magnusson, J. Jalden, H. M. Blau, P. Paul-Gilloteaux, P. Roudot, C. Kervrann, F. Waharte, J.-Y. Tinevez, S. L. Shorte, J. Willemse, K. Celler, G. P. van Wezel, H.-W. Dan, Y.-S. Tsai, C. O. de Solorzano, J.-C. Olivo-Marin, and E. Meijering. Objective comparison of particle tracking methods. Nature Methods, 11(3):281–289, 2014. http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2808.html Ivo Sbalzarini's tracking algorithm as implemented in the MOSAICsuite for Fiji ("Method 1" in the paper) scores very high. In many cases, it is among the top-three performing methods and it is very fast (see Figure 3 in the paper). It performs particularly well in the case for which it has originally been designed: prior-free random motion of viruses. Moreover, its graphical user interface and low number of parameters render it particularly easy to use, thanks to the system integration of Fiji. Prompted by the competition results, we are currently extending the algorithm with additional motion models for directed and constant-speed motion, which should further improve performance, e.g., on the "microtubules" data set. A new release is planned soon and will be distributed through the MOSAIC Fiji Update Site! The algorithm has originally been published in: I. F. Sbalzarini and P. Koumoutsakos. Feature Point Tracking and Trajectory Analysis for Video Imaging in Cell Biology, Journal of Structural Biology 151(2):182-195, 2005. [Less]

The Wiki engine was upgraded and the VisualEditor extension (for in-place editing without having to know MediaWiki markup) was installed. If you encounter a bug, please feel free to describe it carefully in a mail to the Fiji developers' mailing list. Enjoy!

Alexander Krull developed a low light tracking tool while working in Iva Tolic-Norrelykke lab at MPI-CBG. The software is a tool for the tracking of low-light sub-resolution objects in fluorescent microscopy, but it can be applied in other fields as ... [More] well. The plugin implements the localization algorithm described in the following paper: Krull, A., Steinborn A., Ananthanarayanan V., Ramunno-Johnson D., Petersohn U., Tolic-Norrelykke I. M. A divide and conquer strategy for the maximum likelihood localization of low intensity objects. Opt. express, 22, no. 1, pp. 210-228, (2014) As usual it is available through its own update site. Thanks to Alex for this great work and Iva for being such a steadfast supporter of Fiji. [Less]