Automated Axon Tracking of 3D Confocal Laser Scanning Microscopy Images Using Guided Probabilistic Region Merging

Abstract

This paper presents a new algorithm for extracting the centerlines of the axons from a 3D data stack collected by a confocal laser scanning microscope. Recovery of neuronal structures from such datasets is critical for quantitatively addressing a range of neurobiological questions such as the manner in which the branching pattern of motor neurons change during synapse elimination. Unfortunately, the data acquired using fluorescence microscopy contains many imaging artifacts, such as blurry boundaries and non-uniform intensities of fluorescent radiation. This makes the centerline extraction difficult. We propose a robust segmentation method based on probabilistic region merging to extract the centerlines of individual axons with minimal user interaction. The 3D model of the extracted axon centerlines in three datasets is presented in this paper. The results are validated with the manual tracking results while the robustness of the algorithm is compared with the published repulsive snake algorithm.