SNT is both a scripting library and a GUI program. More formally, it is a collection of SciJava commands (add-ons), organized around a common API.
SNT has incorporated several projects that were previously scattered across the Fiji ecosystem of plugins. Notably:
-
Simple Neurite Tracer: The founding ImageJ1 plugin released in 2010. SNT stems from its rewrite. Originally hosted at https://github.com/fiji/SNT
-
Sholl Analysis: Originally hosted at https://github.com/tferr/ASA (Project summary), Sholl Analysis is now part of SNT. Its dedicated documentation page is at https://imagej.net/Sholl
-
hIPNAT: Originally hosted at https://github.com/tferr/hIPNAT (Project summary), hIPNAT is now part of SNT.
An overview of SNT's history is also provided in the FAQs.
SNT is associated with several publications. Please cite the appropriate manuscripts when you use this software in your own research:
The SNT framework is described in:
- Arshadi C, Günther U, Eddison M, Harrington KIS, Ferreira TA. SNT: A Unifying Toolbox for Quantification of Neuronal Anatomy. Nature Methods, 18, 374–377 (2021). https://doi.org/10.1038/s41592-021-01105-7 (PDF
The Sholl Analysis plugin is described in:
- Ferreira T, Blackman A, Oyrer J, Jayabal A, Chung A, Watt A, Sjöström J, van Meyel D. Neuronal morphometry directly from bitmap images, Nature Methods 11(10): 982–984, 2014
Simple Neurite Tracer is described in:
- Longair MH, Baker DA, Armstrong JD. Simple Neurite Tracer: Open Source software for reconstruction, visualization and analysis of neuronal processes. Bioinformatics, 27(17): 2453–54, 2011
- Longair, MH. (2009). Computational neuroanatomy of the central complex of Drosophila melanogaster.
Key aspects of SNT are implemented from published literature:
| Algorithm/Operation | Reference |
|---|---|
| A* search | Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE transactions on Systems Science and Cybernetics, 4(2), 100–107. https://doi.org/10.1109/TSSC.1968.300136 |
| Bi-directional Path Search: Reciprocal cost function | Wink, O., Niessen, W. J., & Viergever, M. A. (2000). Minimum cost path determination using a simple heuristic function. In Proceedings 15th International Conference on Pattern Recognition. ICPR-2000 (3, 998–1001). IEEE. https://doi.org/10.1109/ICPR.2000.903713 |
| Bi-directional A* search (alternate) | Pijls, W.H.L.M. & Post, H., 2009. Yet another bidirectional algorithm for shortest paths, Econometric Institute Research Papers EI 2009-10,Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute. |
| Dijktra's algorithm: Seeded-volume segmentation | Dijkstra, E.W. A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959). https://doi.org/10.1007/BF01386390 |
| Image Processing: Tubeness | Sato, Y., Nakajima, S., Shiraga, N., et al. (1998). Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Medical image analysis, 2(2), 143–168. https://doi.org/10.1016/S1361-8415(98)80009-1 |
| Image Processing: Tubular Geodesics | Türetken, E., Benmansour, F., & Fua, P. (2012). Automated reconstruction of tree structures using path classifiers and mixed integer programming. In 2012 IEEE conference on computer vision and pattern recognition (pp. 566–573). IEEE. https://doi.org/10.1109/CVPR.2012.6247722 |
| Image Processing: Frangi Vesselness | Frangi, A. F., Niessen, W. J., Vincken, K. L., et al. (1998). Multiscale vessel enhancement filtering. In International conference on medical image computing and computer-assisted intervention. MICCAI 1998 (pp. 130–137). https://doi.org/10.1007/BFb0056195 |
| Image Processing: Skeletonization | Arganda-Carreras I., Fernandez-Gonzalez R., Munoz-Barrutia A., et. al. (2010). 3D reconstruction of histological sections: Application to mammary gland tissue. Microscopy Research and Technique, 73(11), 1019–1029. https://doi.org/10.1002/jemt.20829 |
| Convex hull: Volume | Goldman, R. N. (1991). IV.1 - AREA OF PLANAR POLYGONS AND VOLUME OF POLYHEDRA. In J. Arvo (Ed.), Graphics Gems II (pp. 170–171). Morgan Kaufmann. https://doi.org/10.1016/B978-0-08-050754-5.50043-8 |
| Persistent homology: Topological Morphology Descriptor (TMD) algorithm | Kanari, L., Dłotko, P., Scolamiero, M., et al. (2018). A topological representation of branching neuronal morphologies. Neuroinformatics, 16(1), 3–13. https://doi.org/10.1007/s12021-017-9341-1 |
| Persistent homology: Persistence Lanscapes | Bubenik, P. (2015). Statistical Topological Data Analysis Using Persistence Landscapes. Journal of Machine Learning Research, 16(3), 77–102. https://arxiv.org/abs/1207.6437 |
| Longest shortest-path (Graph Diameter) | Bulterman, R.W., van der Sommen, F.W., Zwaan, G., et al. (2002). On computing a longest path in a tree. Information Processing Letters, 81(2), 93–96. https://doi.org/10.1016/S0020-0190(01)00198-3 |
| Cx3D simulation engine | Zubler, F., & Douglas, R. (2009). A framework for modeling the growth and development of neurons and networks. Frontiers in Computational Neuroscience, 3, 25. https://doi.org/10.3389/neuro.10.025.2009 |
| L-measure metrics | Scorcioni, R., Polavaram, S., & Ascoli, G. A. (2008). L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies. Nature Protocols, 3(5), 866. https://doi.org/10.1038/nprot.2008.51 |
| Sholl-based metrics | Ferreira, T., Blackman, A., Oyrer, J. et al. (2014). Neuronal morphometry directly from bitmap images. Nature Methods, 11, 982–984. https://doi.org/10.1038/nmeth.3125 Luis Miguel Garcia-Segura and Julio Perez-Marquez (2014). A new mathematical function to evaluate neuronal morphology using the Sholl analysis. Journal of Neuroscience Methods, 226, 103-109. https://doi.org/10.1016/j.jneumeth.2014.01.016 Milosević, N.T. & Ristanović, D. (2007). The Sholl analysis of neuronal cell images: semi-log or log-log method? Journal of Theoretical Biology 245, 130–140. https://doi.org/10.1016/j.jtbi.2006.09.022 Ristanović, D., Milosević, N.T. & Stulić, V. (2006). Application of modified Sholl analysis to neuronal dendritic arborization of the cat spinal cord. Journal of Neuroscience Methods 158, 2120–218. https://doi.org/10.1016/j.jneumeth.2006.05.030 |
| Distinct colors (SNT's palette of discriminatory colors) | K. Kelly (1965): Twenty-two colors of maximum contrast. Color Eng., 3(6), 1965. (PDF) Paul Green-Armytage, "A Colour Alphabet and the Limits of Colour Coding". Colour: Design & Creativity (5) (2010): 10, 1-23 (PDF) |
| Semantic Segmentation | Arganda-Carreras, I., Kaynig, V., Rueden, C., Eliceiri, K. W., Schindelin, J., Cardona, A., & Sebastian Seung, H. (2017). Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics, 33(15), 2424–2426. https://doi.org/10.1093/bioinformatics/btx180 Arzt, M., Deschamps, J., Schmied, C., Pietzsch, T., Schmidt, D., Tomancak, P., … Jug, F. (2022). LABKIT: Labeling and Segmentation Toolkit for Big Image Data. Frontiers in Computer Science, 4. https://doi.org/10.3389/fcomp.2022.777728 |
Any work that uses data from the supported databases and/or reference brains should acknowledge the data source directly:
| Database | Reference |
|---|---|
| FlyCircuit | Chiang A, Lin C, Chuang C, et al. Three-Dimensional Reconstruction of Brain-wide Wiring Networks in Drosophila at Single-Cell Resolution. Current Biology 21, 1–11 (2011). https://doi.org/10.1016/j.cub.2010.11.056 |
| FlyLight | Jenett A, Rubin GM, Ngo TB et al. A GAL4-Driver Line Resource for Drosophila Neurobiology. Cell Reports, 2, 991–1001 (2012). https://doi.org/10.1016/j.celrep.2012.09.011 |
| InsectBrainDatabase | Heinze S, Jundi B, Berg B, et al. InsectBrainDatabase – A Unified Platform to Manage, Share, and Archive Morphological and Functional Data (2020). https://doi.org/10.1101/2020.11.30.397489 |
| mapzebrain (zebrafish atlas) | Kunst M, Laurell E, Mokayes N, et al. A Cellular-Resolution Atlas of the Larval Zebrafish Brain. Neuron, 103(1), 21–38.e5 (2019). https://doi.org/10.1016/j.neuron.2019.04.034 |
| MouseLight | Winnubst J, Bas E, Ferreira TA, et al. Reconstruction of 1,000 Projection Neurons Reveals New Cell Types and Organization of Long-Range Connectivity in the Mouse Brain. Cell, 179(1), 268–281.e13 (2019). https://dx.doi.org/10.1016/j.cell.2019.07.042 |
| NeuroMorpho | Ascoli GA, Donohue DE, Halavi M. NeuroMorpho.Org: A Central Resource for Neuronal Morphologies. Journal of Neuroscience (35) 9247–9251 (2007). https://dx.doi.org/10.1523/JNEUROSCI.2055-07.2007 |
| Virtual Fly brain | Milyaev N, Osumi-Sutherlandet D, Reeve S, et al. The Virtual Fly Brain Browser and Query Interface. Bioinformatics, 28(3), 411–415 (2012). https://dx.doi.org/10.1093/bioinformatics/btr677 |
Demo datasets (images and/or reconstructions) are either bundled in SNT (and thus part of the source code), or downloaded from the internet:
| Dataset | Source |
|---|---|
| Drosophila ddaC neuron (2D binary image) | Bundled. Sample image for Sholl Analysis/Auto tracing |
| Drosophila OP neuron (3D grayscale image and 'gold standard' reconstruction) | Bundled/Downloaded. DIADEM dataset |
| Hippocampal neuron (2D multichannel image) | Downloaded. Part of ImageJ's samples archive |
| Hippocampal neuron (2D timelapse image with partial reconstruction) | Bownloaded. Cell Image Library dataset |
| L-systems fractal (2D binary image with reconstruction) | Bundled. Generated programmatically |
| Mouse pyramidal neurons (reconstructions) | Bundled. MouseLight dataset |
SNT relies heavily on several SciJava, sciview (and scenery), and Fiji libraries. It also relies on other packages developed under the morphonets umbrella and other external open-source packages. Below is a non-exhaustive list of external libraries on top of which SNT is built:
| Libraries | Scope/Usage |
|---|---|
| 3D Viewer | Legacy 3D Viewer |
| AnalyzeSkeleton, Skeletonize3D | Handling of skeletonized images |
| Apache Commons | Misc. utilities |
| Apache XML Graphics | SVG/PDF export |
| fastutil | High performance, low footprint data structures |
| ImageJ1 | ImagePlus and ROI handling |
| imglib2 | Image representation and processing |
| imagej-plot-service, jfreechart | Histograms and plots including Reconstruction Plotter |
| ImageJ Ops | Image processing and convex hull |
| JGraphT | Graph theory -based analyses |
| JGraphX | Graph Viewer |
| JHeaps | Pathfinding algorithms and data structures |
| JIDE common layer, font awesome, FlatLaf | GUI customizations |
| JSON-Java, okhttp | Access/query of online databases |
| Jzy3D, jGL, JOGL | Reconstruction Viewer |
| pyimagej | Python bindings |
| SMILE | Math and algorithm utilities |
| LabKit, Trainable Weka Segmentation | Semantic segmentation |