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All code for the NeuroMorph Toolkit (RRID:SciRes_000156) is available in open source under the GNU General Public License as published by the Free Software Foundation, and is available for download at cvlab.epfl.ch/NeuroMorph. Example meshes and detailed documentation are also provided at this link. The toolkit functions as an add-on within the Blender open source modeling software (RRID:nif-0000-31943), available at www.blender.org.
Serialelectron microscopy imaging is crucial for exploring the structure of cells and tissues. The development of block face scanning electron microscopy methods and their ability to capture large image stacks, some with near isotropic voxels, is proving particularly useful for the exploration of brain tissue. This has led to the creation of numerous algorithms and software for segmenting out different features from the image stacks. However, there are few tools available to view these results and make detailed morphometric analyses on all, or part, of these 3D models. We have addressed this issue by constructing a collection of software tools, called NeuroMorph, with which users can view the segmentation results, in conjunction with the original image stack, manipulate these objects in 3D, and make measurements of any region. This approach to collecting morphometric data provides a faster means of analysing the geometry of structures, such as dendritic spines and axonal boutons. This bridges the gap that currently exists between rapid reconstruction techniques, offered by computer vision research, and the need to collect measurements of shape and form from segmented structures that is currently done using manual segmentation methods.
Cardona, A., Saalfeld, S., Schindelin, J., Arganda-Carreras, I., Preibisch, S., Longair, M., Tomancak, P., Hartenstein, V., Douglas, R. (2012). TrakEM2 software for neural circuit reconstruction. PLoS One, 7, e38,011. CrossRef
Ciresan, D., Gambardella, L., Giusti, A., Schmidhuber, J. (2012). Deep neural networks segment neuronal membranes in electron microscopy images. NIPS, 2852–2860.
Denk, W., & Horstmann, H. (2004). Serial block-face scanning electron microscopy to reconstruct three-dimensional tissue nanostructure. PLoS Biology, 2, 1864–1877. CrossRef
Hu, T., Nunez-Iglesias, J., Vitaladevuni, S., Scheffer, L., Xu, S., Bolorizadeh, M., Hess, H., Fetter, R., Chklovskii, D. (2013). Electron Microscopy Reconstruction of Brain Structure Using Sparse Representations Over Learned Dictionaries. IEEE Transactions on Medical Imaging, 32, 2179–2188. CrossRef
Jain, V., Bollmann, B., Richardson, M., Berger, D., Helmstaedter, M., Briggman, K., Denk, W., Bowden, J., Mendenhall, J., Abraham, W., Harris, K., Kasthuri, N., Hayworth, K., Schalek, R., Tapia, J., Lichtman, J., Seung, H. (2010). Boundary learning by optimization with topological constraints. IEEE Xplore, 2488–2495.
Ju, T., Losasso, F., Schaefer, S., Warren, J. (2002). Dual contouring of hermite data. ACM Transactions on Graphics (TOG), 21 (3), 339–346. CrossRef
Kreshuk, A., Straehle, C., Sommer, C., Koethe, U., Cantoni, M., Knott, G., Hamprecht, F. (2011). Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images. PLoS One, 6, e24,899. CrossRef
Lucchi, A., Smith, K., Achanta, R., Knott, G., Fua, P. (2011). Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks with Learned Shape Features. IEEE Transactions on Medical Imaging, 30.
Merchán-Pérez, A., Rodriguez, J., Alonso-Nanclares, L., Schertel, A. (2009). Counting Synapses Using FIB/SEM Microscopy: A True Revolution for Ultrastructural Volume Reconstruction. Frontiers in Neuroanatomy, 3.
Straehle, C., Köthe, U., Knott, G., Hamprecht, F. (2011). Carving: scalable interactive segmentation of neural volume electron microscopy images. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2011, 14, 653–660. CrossRef
Vazquez-Reina, A., Gelbart, M., Huang, D., Lichtman, J., Miller, E., Pfister, H. (2011). Segmentation fusion for connectomics. IEEE Xplore, 177–184.
- NeuroMorph: A Toolset for the Morphometric Analysis and Visualization of 3D Models Derived from Electron Microscopy Image Stacks
- Springer US
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