Automatic Neuron Tracing Using a Locally Tunable Approach

Neuroscience has been interested in cellular neuroanatomy since the time of Ramón y Cajal. Although the manual reconstruction of neuron morphologies is still widely used, the recent availability of large image data asks for automatic or semi-automatic tools. In this paper we present an automatic method to trace neurites in 3D volumes based on two main steps. The first detects neurites connected with a given seed that satisfy a conservative membership rule, the second detects weak neurite chunks allowing a local growth of the arbor on the basis of local intensity features. The local step also accommodates to the nonuniform illumination and to the noise of the sample. We tested our proposal on the Olfactory Projection dataset belonging to the well-known DIADEM challenge, comparing its performance against those achieved by other state-of-the-art methods available within the BigNeuron Project.