Improved detection of branching points in algorithms for automated neuron tracing from 3D confocal images

Abstract

Automated tracing of neuronal processes from 3D confocal microscopy images is essential for quantitative neuroanatomy and neuronal assays. Two basic approaches are described in the literature-one based on skeletonization and another based on sequential tracing along neuronal processes. This article presents algorithms for improving the rate of detection, and the accuracy of estimating the location and process angles at branching points for the latter class of algorithms. The problem of simultaneously detecting branch points and estimating their measurements is formulated as a generalized likelihood ratio test defined on a spatial neighborhood of each candidate point, in which likelihoods were computed using a ridge detection approach. The average detection rate increased from from 37 to 86%. The average error in locating the branch points decreased from 2.6 to 2.1 voxels in 3D images. The generalized hypothesis test improves the rate of detection of branching points, and the accuracy of location estimates, enabling a more complete extraction of neuroanatomy and more accurate counting of branch points in neuronal assays. More accurate branch point morphometry is valuable for image registration and change analysis.