Segmentation, tracking, and tracing of neurons in video imagery are important steps in many neuronal migration studies and can be inaccurate and time-consuming when performed manually. In this paper, we present an automated method for tracing the leading and trailing processes of migrating neurons in time-lapse image stacks acquired with a confocal fluorescence microscope. In our approach, we first locate and track the soma of the cell of interest by smoothing each frame and tracking the local maxima through the sequence. We then trace the leading process in each frame by starting at the center of the soma and stepping repeatedly in the most likely direction of the leading process. This direction is found at each step by examining second derivatives of fluorescent intensity along curves of constant radius around the current point. Tracing terminates after a fixed number of steps or when fluorescent intensity drops below a fixed threshold. We evolve the resulting trace to form an improved trace that more closely follows the approximate centerline of the leading process. We apply a similar algorithm to the trailing process of the cell by starting the trace in the opposite direction. We demonstrate our algorithm on two time-lapse confocal video sequences of migrating cerebellar granule neurons (CGNs). We show that the automated traces closely approximate ground truth traces to within 1 or 2 pixels on average. Additionally, we compute line intensity profiles of fluorescence along the automated traces and quantitatively demonstrate their similarity to manually generated profiles in terms of fluorescence peak locations.
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