Rapid automated three-dimensional tracing of neurons from confocal image stacks

Algorithms are presented for fully automatic three-dimensional (3D) tracing of neurons that are imaged by fluorescence confocal microscopy. Unlike previous voxel-based skeletonization methods, the present approach works by recursively following the neuronal topology, using a set of 4 /spl times/ N/sup 2/ directional kernels (e.g., N = 32), guided by a generalized 3D cylinder model. This method extends our prior work on exploratory tracing of retinal vasculature to 3D space. Since the centerlines are of primary interest, the 3D extension can be accomplished by four rather than six sets of kernels. Additional modifications, such as dynamic adaptation of the correlation kernels, and adaptive step size estimation, were introduced for achieving robustness to photon noise, varying contrast, and apparent discontinuity and/or hollowness of structures. The end product is a labeling of all somas present, graph-theoretic representations of all dendritic/axonal structures, and image statistics such as soma volume and centroid, soma interconnectivity, the longest branch, and lengths of all graph branches originating from a soma. This method is able to work directly with unprocessed confocal images, without expensive deconvolution or other preprocessing. It is much faster that skeletonization, typically consuming less than a minute to trace a 70 MB image on a 500 MHz computer. These properties make it attractive for large-scale automated tissue studies that require rapid on-line image analysis, such as high-throughput neurobiology/angiogenesis assays, and initiatives such as the Human Brain Project.