The SOA is an automated tool that has a user-friendly interface, for identification, segmentation, and extraction of important morphological information from images of complex 2D line networks. The work process is simple and intuitive and the data is obtained immediately and easily. Moreover, because SOA is adaptable and flexible, its analytic capacity can be expanded for other applications.
SOA can be used for the analysis of other types of 2D networks, such as networks of non-neural cells, complex structures generated by the cytoskeleton, and non-biological networks, like nanotubes. Open the webpage, find the SOA. zip zipped folder, and download the zip file by double clicking.
Unzip the folder by right clicking and selecting extract files. Observe the extraction path in the options window that opens in the destination address text box that displays the path for the extracted files. Next, open the extracted SOA file and double click on SOA.exe.
Wait for a black window to open, after which the application will appear. In the SOA viewer upload menu bar, select choose file, then choose an image from the computer files and click on it. Click open, observe the file's path, then click next.
For segmentation optimization in edges, adjust the threshold for the display by selecting threshold and entering a number. In merge lines, adjust the minimum distance to merge for the display by selecting the min distance to merge and entering a number, and the minimum angle to merge for the display by selecting the min angle to merge and entering a number. Then, click on create preview segmentation image and change the parameters to achieve maximum identification of segments.
If properties need to be changed, click on the close window button and readjust the threshold in the minimum distance and angle to merge. To create the output files, press okay to visualize the segmentation images in the analyzing graphs. In the window that appears, select a location where the xlsx file will be saved.
Insert a file name, then choose save and wait for the xlsx file with data to be created and saved. To navigate back and forth between previously defined views, use the forward and back buttons. Activate panning and zooming by pressing the zoom button, then move the mouse to the desired location.
Then, pan the figure by pressing and holding the left mouse button while dragging it to a new position. Release the mouse button and the selected point in the image will appear in the new position. Hold down the X or Y keys to restrict the motion of the axes.
To zoom, hold down the right mouse button and drag it to a new location. Move right to zoom in on the x-axis and move left to zoom out on the x-axis. Do the same for the y-axis in up and down motions.
When zooming, note that the point under the mouse remains stationary, making it possible to zoom in or out around that point. Use the modifier keys, X, Y, or Control, to limit the zoom to the X, Y, or aspect ratio preserve respectively. To activate the zoom to rectangle mode, click the zoom to rectangle button.
Place the cursor over the image and press the left mouse button. Drag the mouse to a new location while holding the button to define a rectangular region. Use the subplot configuration tool to configure the appearance of the subplot.
To open a file save dialogue, click the save button and save the file in the appropriate formats. A typical SOA workflow is applied to a representative 2D image of a dendritic network labeled with a fluorescent anti-MAP2 antibody. The SOA's GUI enables comparison of the original image to the segmented image and provides realtime monitoring of the effect of any changes to the segmentation settings.
The dendritic branches are classified as growing parallel and non-parallel. Once the analysis is complete the number of parallel branches within the tested range is extracted and plotted in a frequency graph. To understand whether the extent of parallel growth among the dendritic branches is random or directed, the results of this graph are compared to those extracted from simulation of random growth of lines of the same number as those of the dendritic branches in the cultures.
The SOA then measures the distances among the parallel branches, as well as the lengths of the parallel and non-parallel dendritic branches and their average lengths. To determine whether preferential growth directions exist, the SOA displays a distribution histogram of the growth angles of the dendritic branches, allowing rapid identification of preferred growth directions and specific dendritic branches in each group. The SOA output can be used as a database for tool that perform more in-depth and complex analyses.
