Improving the Usability of Virtual Reality Neuron Tracing with Topological Elements

Torin McDonald, Will Usher, Nate Morrical, Attila Gyulassy, Steve Petruzza, Frederick Federer, Alessandra Angelucci, Valerio Pascucci

View presentation: 2020-10-28T16:30:00Z GMT-0600 Change your timezone on the schedule page
Exemplar figure
Left to right: A connected graph of ridge-like structures is extracted from the Morse-Smale complex (MSC), containing a superset of the possible neuron segments in the data. Our MSC-guided semi-automatic tracing tool enables users to rapidly trace paths and view a live preview as they do so (orange line). When satisfied with the trace, they can add it to the reconstruction (white line).
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Application Motivated Visualization, Life Sciences, Health, Medicine, Biology, Bioinformatics, Genomics, Computational Topology-based Techniques


Researchers in the field of connectomics are working to reconstruct a map of neural connections in the brain in order to understand at a fundamental level how the brain processes information. Constructing this wiring diagram is done by tracing neurons through high-resolution image stacks acquired with fluorescence microscopy imaging techniques. While a large number of automatic tracing algorithms have been proposed, these frequently rely on local features in the data and fail on noisy data or ambiguous cases, requiring time-consuming manual correction. As a result, manual and semi-automatic tracing methods remain the state-of-the-art for creating accurate neuron reconstructions. We propose a new semi-automatic method that uses topological features to guide users in tracing neurons and integrate this method within a virtual reality (VR) framework previously used for manual tracing. Our approach augments both visualization and interaction with topological elements, allowing rapid understanding and tracing of complex morphologies. In our pilot study, neuroscientists demonstrated a strong preference for using our tool over prior approaches, reported less fatigue during tracing, and commended the ability to better understand possible paths and alternatives. Quantitative evaluation of the traces reveals that users' tracing speed increased, while retaining similar accuracy compared to a fully manual approach.