Zview Manual
Calfu Baransky
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Image/Image file The image to be traced/analyzed. The drop-down menu will list all images currently open in ImageJ. Alternatively, an image path may be specified by clicking Browse and choosing an image file. If no image is chosen, SNT will create an empty display canvas from the computed bounding box of the reconstruction file (if provided).
Reconstruction file The path of the reconstruction file to be imported. SNT will automatically try to fill this field by looking at all the reconstruction files (.traces, .(e)swc, .ndf, or .json) in the image directory and retrieving the filename closer to Image file.
User interface Specifies which views to display for 3D images. The default setting provides the XY, ZY, and XZ views and allows for more accurate node placement but requires more RAM.
All SNT dialogs (including the Neuroanatomy Shortcut Window support drag-and-drop: E.g., you can import SWC files just by dragging and dropping them either into the main SNT dialog or the Path Manager.
This option assumes you are tracing on the same spatial coordinates of an annotated neuropil for which compartments have been segmented (labeled) and stored in an Amira labels file. Once loaded, SNT will report the name of the compartments in the ImageJ status bar when hovering over the image.
Options to save/export all of traced paths in the SWC and TRACES formats. Note that it is also possible to export subsets of paths using the Path Manager.This menu also includes controls to create SNAPSHOT BACKUPS. To use this feature:
Allows morphometric comparisons of two single reconstruction files or multiple groups of reconstruction files (including statistical reports and two-sample t-test/one-way ANOVA analysis). Color-coded montages of analyzed groups can also be generated. See Comparing Reconstructions for details.
To facilitate accurate positioning of starting points, so that they are really on a neurite rather than close to one, the program carries out a local snapping operation. This means that when moving the mouse within the image, the program quickly searches in a small window around the current mouse position for the pixel that is most likely to be on a neurite.
Enable A* search algorithm By default, SNT uses the A* search algorithm to automatically trace paths between two manually selected points. To manually place nodes along a path, toggle this feature off. Note that it is also possible to enable other built-in algorithms or algorithms provided by external SNT add-ons. Current options include:
Independently of the algorithm used, the algorithm drop-down menu in this pane provides options to tweak the performance, accuracy and footprint of the computations involved in the search. These include:
Cost Function Auto-tracing algorithms aim to find a path to the destination node under the smallest cost of deviating from the signal along a neurite. A successful search between two points is thus the cheapest path with the least deviations. This is implemented through a cost function in which voxels along a neurite are assigned lower costs, while voxels outside the neurite are assigned higher costs or penalties. SNT implements several cost functions, namely:
Specify Manually Advanced option for users with if a quantitative understanding of the image. Searches may consider pixels outside neurites when maximum in over-estimated, and may take significantly longer when it is under-estimated, since each pixel will carry a greater-than-reasonable cost
Compute Once Statistics are computed once for the whole image (may take a while for large images) and stored in memory. If enough RAM is available this may speed up searches. Although in real-word usage any speed-gains relatively to Compute Real-Time seem negligible on modern hardware
Tracing on Secondary Layer is the answer to these questions: It allows you to feed SNT with a pre-processed image on which the A* star search will operate. Because this option can be toggled at will, it becomes a secondary layer for auto-tracing: E.g., you may decide to auto-trace certain neurites on the original image, while tracing other neurites on the secondary layer.
For the most part, the secondary layer remains hidden because feedback on auto-tracing searches is always provided in the original image. When RAM is not limited, one can ping-pong between secondary and original image simply, by pressing L, the shortcut for the Trace/Fill on Secondary Layer checkbox. Here are some specific usages for this feature:
Image Processing at multiple scales Consider a structure formed simultaneously by very thick and very thin processes (e.g. axons and dendrites). To trace structures of variable diameters more effectively, one could pre-filter a copy of the current image at multiple scales, and load the result as secondary layer
Probability maps (p-maps) Probability maps of classified images (generated using e.g., machine learning) may not be 100% accurate. We find that, in many cases it is more effective to use p-maps as secondary layers, rather than attempting fully automated p-map reconstructions that typically require time-consuming post-hoc corrections
The wizard needs two types of information from the user: The type of filtering operation and the size(s) (scale(s)) of the structures being traced, which control the spatial scale of the filter (known as σ).
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