Pipeline for 3D segmentation and analysis of biological sample images
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General notes

This is the code repository for project "Parismi", a 3D image segmentation pipeline designed for biological images. Parismi is detailed in reference [1], and was also used in references [2-4]. Note that although Parismi is functional, user friendliness is not an area to which we have been able to devote much effort so far. As discussed in [1], the ideas demonstrated by this implementation might be usefully translated to other software ecosystems (which should be made relatively straightforward by the release of our code).

Running Parismi

Parismi can be run in interactive mode, as an ImageJ plugin. To give it a quick try, download the release archive, open Parismi/IJ/ij.jar and select A0PipelineManager from the plugin menu. Pipelines available in released datasets can be loaded by drag and drop of corresponding .xml files onto the Parismi control panel. See Parismi operation overview for more detail.

Parismi can also be run in batch mode, using the same pipelines that can be edited and run from the GUI. Our datasets come with a system of Makefiles that automate this process (see released datasets).

Much of Parismi’s functionality is implemented as integrated Java plugins, which should work out of the box on a number of platforms. Some important plugins — such as the active contour plugin — are implemented in C++ and thus require a system-specific binary (x86-64 binaries are provided for Mac OS X and FreeBSD, and will be made available for Debian GNU/Linux shortly; these libraries require dependencies to have been installed; see below). The cell detector plugin requires a Matlab runtime.

Functional tests

A sample test set is provided, which covers a number of Parismi plugins. Go to Parismi/functional_tests/test_datasets/0/ and run GNU make (you may need to compile the C++ plugins first; see below). If you want to test the cell detector, you need to install a Matlab runtime of the right version (currently R2014b for OS X and R2014a for Linux) or compile the detector yourself from Matlab; using the Linux binary provided at cell_detector/bin/detect_executable_linux64_MCR_R2014a requires adjusting the script IJ/matlab_interface/matlab_wrapper.

Future developments

Note that there are features of Parismi that are not yet reported in companion papers; many of these features are still work in progress. An area of future development will be overhauling of the GUI pipeline editor to make it graph-based.


  • Operating system: Debian GNU/Linux (or most probably any other Linux distribution as long as libdispatch is installed), Mac OS X, or FreeBSD (or probably any other UNIX). These operating systems are listed by increasing order of testing Parismi has received. No Microsoft Windows version is available, although Parismi could probably be made largely functional under Windows with fairly minor modifications.

  • Java >= 8 (package openjdk-8-jdk under Debian).

  • A Matlab runtime to run the automatic cell detection plugin (free binaries available from MathWorks for Linux and Mac OS X).

  • The following packages should be installed under Debian: libusb-1.0.0, libdc1394-22, libdc1394-22-dev, libtiff5-dev. For compilation purposes, the following packages should also be installed: git, ant, libdispatch-dev, libboost-dev, libprotobuf-dev, protobuf-c-compiler, protobuf-compiler. Mac OS X equivalents that can be installed with MacPorts are libdc1394, libusb, tiffandgit-core, apache-ant, boost, protobuf-cpp.

  • Lots of RAM to deal with large images (Parismi was not optimized to minimize RAM usage; our machines commonly have 16GB of RAM or more).

  • As many CPUs and CPU cores as possible (Parismi tries to make use of as many cores as reported available by the hardware).

  • A C++ compiler, with block support if you wish to get parallelization support (clang is probably the best choice), and some open-source libraries listed below to compile the C++ plugins.


A disk image of a Debian installation of Parismi will be provided at a later time. This image will provide an installation of Parismi that will work out of the box on any machine that can run Sun/Oracle’s free VirtualBox.

If a JNA library is on the Java classpath it needs to be version 4 or above. The presence of an older version of the native code part of the library can prevent communication between the Java pipeline and the C++ plugins.

To compile the Java pipeline manager, run ant in the A0PipeLine_Manager directory. This produces the file A0PipeLine_Manager.jar, in which all dependencies are packaged and that can be run in standalone fashion or act as an ImageJ plugin when placed in the IJ/plugins directory.

To compile the C++ plugins, edit the library and include paths in the Makefile in the C++_plugins directory if need be, and run GNU make. This produces a libsegpipeline_1.X file that should be copied into the relevant architecture-specific subdirectory in IJ/native_libs, and a standalone executable that serves debugging purposes.

To compile the Matlab cell detection plugin, see the cell_detector directory. Please adjust IJ/matlab_interface/matlab_wrapper to point at the resulting executable.


We have made every effort not to reinvent the wheel and thus reused code from a number of open source projects, either as libraries packaged with Parismi or as source code modified for our purposes (kept for the most part under “contrib” in our source trees). This includes in particular:

  • ImageJ by Wayne Rasband / Fiji: Parismi can be run as an ImageJ plugin, from a customized version of ImageJ v1.43 that is distributed along with Parismi (ImageJ is used to display images, and all ImageJ functionality is available). The ImageJ code was tweaked to allow integration with our annotation GUI, allow clickable orthogonal views of composite images (modifying some code by Dimiter Prodanov), add some GUI shortcuts, and resolve some concurrency issues. Some ImageJ and Fiji plugins were slightly modified to improve performance with multithreading and to allow integration with Parismi. Parismi’s TIFF image reading and writing was adapted from ImageJ, with performance improvements, the use of the BigTIFF file format to allow for larger file sizes, and native reading of Zeiss LSM files.

  • Some ImageJ/Fiji plugins were adapted for use with Parismi, and sometimes multi-threaded in the process; this includes skeletonization, blob-finding, and hole-filling plugins by Ignacio Arganda-Carreras, Mark Longair, and Stephan Preibisch. The Z projector plugin (by Patrick Kelly) was more substantially modified.

  • Principal curves by Balázs Kégl: reused with very minor modifications to define a path along the distal-proximal axis of the C. elegans gonad.

  • Implementation of kd trees by Simon Levy: used to efficiently find neighbors of segmentation seeds.

  • [Ellipse fitter by Maurizio Pilu](http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/PILU1/ ElliFit.java): used to fit ellipses on images of C. elegans embryos. Slightly modified to decouple algorithm code from GUI and to report ellipse characteristics.

  • Apache Commons Primitives library: used to provide number lists backed by arrays of primitives rather than arrays of Number objects, minimizing the performance and memory impact of large lists of numbers.

  • JCommander by Cédric Beust: used to parse command-line arguments.

  • JFreeChart by by David Gilbert: used to produce histograms and pairwise plots to allow for interactive exploration of quantification results.

  • Google’s Protobuf: used to to store segmentations to disk in a space-efficient fashion, and to pass segmentations back and forth between Java and C++ code.

  • Protobuf patch by Ryan Fogarty: we changed from custom modifications to the Java code generated by the protobuf compiler to this patch, which modifies the protobuf compiler so that lists of numbers are backed by arrays of primitives.

  • Google’s Guava: used to transparently cache data read from image files that are too large to fit in RAM.

  • Expr4J by Peter Smith: used to provide spreadsheet-like functionality to explore quantification results, which is still in a highly-experimental and unfinished state.

  • libdc1394 by Damien Douxchamps: used to read images directly from FireWire cameras with an IEEE1394 interface.

  • Our distribution of ImageJ comes with a few plugins pre-installed that we find convenient in our daily usage; notably an image stitching plugin (Preibisch, S., Saalfeld, S., and Tomancak, P. (2009). Globally optimal stitching of tiled 3D microscopic image acquisitions. Bioinformatics 25, 1463–1465.) and dependencies including ImgLib, and the LOCI Bio-Formats Importer.

  • [3D Bresenham's line generation code](http://www.mathworks.com/matlabcentral/fileexchange/21057-3d- bresenham-s-line-generation) by Jimmy Shen

  • [Code for level set re-initialization by the Sussman method](http://www.mathworks.com/matlabcentral/fileexchange/30284-active -contours-implementation---test-platform-gui/content/Activeontours/ localized_seg.m) by Nikolay S.

  • Eigenvalue and eigenvector computation code by NIST, Mathworks

  • Code to generate randomly-distributed vectors on a sphere by Jonathan D. Lettvin

  • Code for fast distance transform by Pedro Felzenszwalb

  • Other libraries part of our distribution include XStream, JNA, SwingX, and Eclipse annotations.

  • Silk icons by Mark James are used in the active contour toolbar (Creative Commons Attribution 2.5 License).


Parismi uses code from projects listed above, which come under various licenses (GPL, BSD, Apache, etc.) or are in the public domain. The GPL is the lowest common denominator. We are dual-licensing Parismi-specific code and modifications to pre-existing code on a file-by-file basis, under either the GPL or the BSD two-clause license. In other words, anyone can freely modify Parismi’s code and redistribute it under the GPL. Anyone who removes GPL-licensed code from Parismi should be able to distribute the modified project under a BSD-like license.

Parismi uses YourKit for performance analysis and debugging of memory leaks. YourKit supports open source projects with its full-featured Java Profiler. YourKit, LLC is the creator of YourKit JavaProfiler and YourKit .NET Profiler. innovative and intelligent tools for profiling Java and .NET applications. YourKit


[1]: Chiang M., Hallman S., Cinquin A., Reyes de Mochel N., Paz A., Kawauchi S., Calof A.L., Cho K.W., Fowlkes C.C. & Cinquin O. (2015) Analysis of in vivo single cell behavior by high throughput, human-in-the-loop segmentation of three-dimensional images. BMC Bioinformatics (in press).

[2]: Reyes de Mochel N.S., Luong M., Chiang M., Javier A.L., Luu E., Toshihiko F., MacGregor G.R., Cinquin O. & Cho K.W. (2015) BMP signaling is required for cell cleavage in preimplantation-mouse embryos. Dev Biol 397(1), pp45-55

[3]: Cinquin A., Zheng L., Taylor P.H., Paz A., Zhang L., Chiang M., Snow J.J., Nie Q. & Cinquin O. (2015) Semi-permeable diffusion barriers enhance patterning robustness in the C. elegans germ line. Dev Cell (in press).

[4]: Chiang M., Cinquin A., Paz A., Meeds E., Price C.A., Welling M. & Cinquin O. (2015) Control of Caenorhabditis elegans germ-line stem-cell cycling speed meets requirements of design to minimize mutation accumulation. BMC Biology 13:51.