NeuronStudio was a non-commercial program created at Icahn School of Medicine at Mount Sinai by the Computational Neurobiology and Imaging Center. This program performed automatic tracing and reconstruction of neuron structures from confocal image stacks. The resulting models were then be exported to file using standard formats for further processing, modeling, or for statistical analyses. NeuronStudio handled morphologic details on scales spanning local Dendritic spine geometry through complex tree topology to the gross spatial arrangement of multi-neuron networks. Its capability for automated digitization avoided the subjective errors inherent in manual tracing. The program ceased to be supported in 2012 and the project pages were eventually removed from the ISMMS Website. Its documentation and the Windows source code however are still available via the Internet Archive.
Deconvolution
Deconvolution of imaged data is essential for accurate 3D reconstructions. Deconvolution is an image restoration approach where 'a priori' knowledge of the optical system in the form of a point spread function (PSF) is used to obtain a better estimate of the object. A point spread function can be either calculated from the actual microscope parameters, measured with beads, or estimated and iteratively refined (Blind deconvolution). PSFs can be adjusted locally to account for variations in refractive characteristics of the tissue with depth and sample characteristics. For automated use with large, tiled tissue blocks, this is faster and more accurate than using an experimentally determined PSF.
Skeletonization and diameter estimation
Quantization errors arise in standard skeletonization algorithms from the integer nature of digital images. The requirement for accurate representation of fine dendritic geometry has required the development of novel adaptations of standard skeletonization and diameter estimation algorithms to correct for these quantization errors. Iterative thinning skeletonization methods can provide a distance in voxels from each tree node to the surface of the object. This distance is the D6 metric, obtained by counting the number of voxels as they are removed in the minimal 6-connected path from the surface to the medial axis. In existing skeletonization or vectorization algorithms for dendritic morphometry, the branch cross-section at any node is approximated as circular, with the D6 metric providing the single diameter estimate. The precision of this diameter estimate is limited to the physical size of the voxels. For small structures such as thin dendrites and spines, comprising only a few voxels even at maximal imaging resolution, the error can be significant if this measure is used directly (see figure). To minimize quantization error and evaluate more precisely the geometry of the nodes, a new estimation technique exists, the Rayburst Sampling Algorithm that uses the original grayscale data rather than the segmented images for precise, continuous radius estimation, and multidirectional radius sampling to more accurately represent non-circular branch cross-sections and non-spherical spine heads.