I am working on a project that involves detailed neuron tracing using Neurolucida 360; and I could use some advice on optimizing the software for complex neuron structures. My dataset includes neurons with intricate branching patterns; and while Neurolucida 360 has been incredibly helpful; I am encountering a few challenges.
Some of the neurons have very fine branches that the software seems to struggle with. Despite adjusting the sensitivity settings, the tracing either misses these fine details or overextends into areas that don’t accurately represent the neuron’s structure. Has anyone else experienced this? Are there specific settings or techniques you recommend to improve tracing accuracy for such complex neurons?
I am also trying to strike a balance between processing speed and the precision of the tracings. Increasing the precision naturally slows down the process; but I am wondering if there are ways to optimize performance without sacrificing too much accuracy. Are there certain hardware configurations or software settings that could help with this?
After completing the initial tracings; I often find that I need to go back and manually adjust some areas. I am curious about best practices for post-processing within Neurolucida 360. Are there any tools or workflows within the software that can help streamline this process?
Thank you in advance for your help and assistance.
What settings or techniques can improve the automatic detection of thin neurons if adjusting the seed sensitivity still does not detect those fibers? a. Adjust the image histogram
Often, thinner branches do not have as strong of a signal as thicker branches. If this is the case for your image, the fibers may be so close to the background that even increasing the seed sensitivity is not enough to detect these thin fibers. Adjustments to the image histogram do impact the detection algorithms for all tracing modes. To improve the contrast for the thinner neurons, open the Image Adjustment Panel found in the 2D window’s Image ribbon. Try increasing the black point and/or decreasing the white point on the histogram to improve the contrast of these thinner neurons in comparison to the background. Try detecting your seeds again in the automatic tracing tab to see if this improves the detection of the thin neurons. b.Decrease the Typical Process Width
When there is variation in branch thickness throughout an image, the automatic tracing algorithm may filter out and not detect branches of a certain thickness based on the Typical Process Width (average branch diameter) value automatically calculated for each image. This setting is located in the user-guided tracing tab.
To identify the diameter of the thinner branches the automatic tracing algorithm does not detect, use the Measure Line tool found in the 2D window’s Trace ribbon. Using the measured diameter information, adjust the Typical Process Width value in the 3D window’s user-guided tracing tab. Toggle to the automatic tracing option to detect your seeds. I expect that your finer neurons will be detected as seeds now.
How can you optimize neuron tracing speeds without sacrificing accuracy? Are there certain hardware configurations or software settings that could help with this? a. Recommended Specifications for Neurolucida 360
All MBF Bioscience products list the recommended PC specifications on the software’s webpage. Select the Specification tab to see the recommended specifications for Neurolucida 360. b. Other Considerations
Image size, number of channels, bit depth, number of Z planes, and format can also play a role in automatic detection speeds. Recommended PC specifications will vary based on your image data. However, all MBF Bioscience software is optimized to work best with JP2000 images. MBF Bioscience offers a free image converter, Microfile+, that can help convert proprietary microscopy image formats to this preferred format using a batch mechanism. In addition to considering optimizing your computer specifications, I would recommend converting to this optimized image format to improve detection speeds.
What tools can help streamline the auditing process following an automatic neuron tracing? a. Cleanup Editing
I recommend trying the Cleanup Editing Tool. The tool identifies, lists, and highlights possible fragments within the tracing. By selecting one, you can view the fragment location and determine if any auditing is required to ensure the tracing accurately represents the image data. b. Subvolume Visualization
The Subvolume Tool can be used to break up your image into smaller chunks. You can automatically trace each chunk one at a time if it makes sense to use different tracing parameters for different locations within the image. Additionally, you can use this tool to review a small chunk of your tracing at a time for focused auditing. I like to work in a meander scan pattern with a small overlap between each subvolume to ensure I don’t miss any tracings that need to be reviewed.
I hope these suggestions help improve the automated tracing results, speeds, and editing process. Feel free to post any follow-up questions on this thread.
Thanks again for bringing such fantastic questions to our forum!