Just Go with the Optical Flow: Enhanced Axon Tracing by Modeling Fiber Connections as Motion

In this work, we explore the extension of motionbased techniques from computer vision into the realm of biological structure detection. Specifically, we propose using optical flow as a feature for guiding dense axon centerline detection and tracing. Optical flow is a technique that describes the apparent movement of objects over time, as in a video. Our experiments show that interpreting 3D volumes as videos, by interchanging the time axis of optical flow with the x, y, and z axes of the volumes, extracts useful spatial relationships in the context of axon centerline detection. This approach offers several advantages. First, optical flow captures subtle spatial gradients, providing a rich representation of directional changes in intensity, which helps delineate the linear structure of axons. Second, by leveraging the continuity of flow vectors, our method enhances the detection of axon trajectories even in areas with low contrast or noise. Our experiments demonstrate that this adaptation of optical flow yields superior results in centerline detection compared to traditional edge-based or segmentation methods. To validate our claims, we show performance on two axon tracing datasets. By interpreting optical flow in this multidimensional space, we offer a new perspective on how spatial dynamics can inform 3D tracing tasks.