Improving the Usability of Virtual Reality Neuron Tracing with Topological Elements

TL;DR

This paper introduces a semi-automatic neuron tracing method using topological features within a VR environment, improving speed and user experience while maintaining accuracy in connectomics research.

Contribution

The authors present a novel topological-guided semi-automatic neuron tracing technique integrated into VR, enhancing usability and efficiency over traditional manual methods.

Findings

Users traced neurons faster with the new method.

Participants preferred the VR tool over previous approaches.

Tracing accuracy was maintained despite increased speed.

Abstract

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.