3D Automated Reconstruction Tool for Vesicle Strucures of Electron Tomograms
Automatic image reconstruction is critical to cope with steadily increasing data from
advanced microscopy. We describe here the Fiji macro 3D ART VeSElecT which we developed
to study synaptic vesicles in electron tomograms. We apply this tool to quantify vesicle
properties (i) in embryonic Danio rerio 4 and 8 days past fertilization (dpf) and (ii) to compare
Caenorhabditis elegans N2 neuromuscular junctions (NMJ) wild-type and its septin mutant
(unc-59(e261)). We demonstrate development-specific and mutant-specific changes in synaptic
vesicle pools in both models. We confirm the functionality of our macro by applying our
3D ART VeSElecT on zebrafish NMJ showing smaller vesicles in 8 dpf embryos then 4 dpf,
which was validated by manual reconstruction of the vesicle pool. Furthermore, we analyze
the impact of C. elegans septin mutant unc-59(e261) on vesicle pool formation and vesicle
size. Automated vesicle registration and characterization was implemented in Fiji as two
macros (registration and measurement). This flexible arrangement allows in particular
reducing false positives by an optional manual revision step. Preprocessing and contrast
enhancement work on image-stacks of 1nm/pixel in x and y direction. Semi-automated cell
selection was integrated. 3D ART VeSElecT removes interfering components, detects vesicles
by 3D segmentation and calculates vesicle volume and diameter (spherical approximation,
inner/outer diameter). Results are collected in color using the RoiManager plugin
including the possibility of manual removal of non-matching confounder vesicles. Detailed
evaluation considered performance (detected vesicles) and specificity (true vesicles) as
well as precision and recall. We furthermore show gain in segmentation and morphological
filtering compared to learning based methods and a large time gain compared to manual
segmentation. 3D ART VeSElecT shows small error rates and its speed gain can be up to
68 times faster in comparison to manual annotation. Both automatic and semi-automatic
modes are explained including a tutorial.
Together with the macros "3DART_VeSElecT_RegistVesicle" and "3DART_VeSElecTMeasureVesicle" we provide a Fiji version (1.51g for Linux operating systems) on this homepage.
We succsessfully tested the macros for Fiji version 1.51g for different operating systems (Linux, Windows and Macintosh), which is the latest version so far.
Still, it is recommended to use the here provided version in combination with 3D ART VeSElecT for two reasons. On the one hand, this version already includes an additional plugin (3D ImageJ Suite), on the other hand, we can not guarantee the flawless function of the macro for other versions, e.g. in case of updates.
If you would like to test 3D ART VeSElecT we provide a test stack and a user description for download.
Click HERE to download the macros "3DART_VeSElecT_RegistVesicle" and "3DART_VeSElecTMeasureVesicle" as zip file.
Click HERE to download the user description "How_to_use_3D_ART_VeSElecT".
Click HERE to download the test_stack.tif of an electron microscopic tomogram.
Click HERE to download FIJI (1.51g for Linux operating systems, includes 3D ImageJ Suite).
Notice: If a different FIJI version than the one which is provided above is used, additionally the "3D ImageJ Suite" plugin has to be installed (for more information about 3D ImageJ Suite click HERE).
For more information about FIJI visit: www.fiji.sc
Please cite this reference if you use 3D ART VeSElecT:
Kaltdorf, K.V. et. al., 2017. FIJI Macro 3D ART VeSElecT: 3D Automated Reconstruction Tool for Vesicle Structures of Electron Tomograms., pp. 1-21.
(Download Paper as PDF, click HERE)
FIJI is provided by:
Schindelin, J. et al., 2012. Fiji: an open-source platform for biological-image analysis. Nature methods, 9(7), pp.676–82. Available at: http://dx.doi.org/10.1038/nmeth.2019
3D ImageJ Suite:
J. Ollion, J. Cochennec, F. Loll, C. Escudé, T. Boudier. (2013) TANGO: A Generic Tool for High-throughput 3D Image Analysis for Studying Nuclear Organization. Bioinformatics 2013 Jul 15;29(14):1840-1.