Convolutional Neural Network Cascade Based Neuron Termination Detection in 3D Image Stacks

Full reconstruction of neuron morphology in volumetric images is of fundamental interest for the analysis and understanding of neuron function. Termination points could be very good candidates of seeding points for neuron reconstructing (tracing) applications. Previously, some hand-crafted models were proposed to detect the neuron terminations. However, they are highly depending on empirical setting of the parameters for different images. In this paper, we propose a neuron termination detection approach with two-level designed convolutional networks. At first level, a Triple-Crossing 2.5D convolutional neural network with inception block and residual block is used to generate the termination candidate points by early identification of ‘many’ volumetric patches. At second level, those termination candidates are further evaluated by using the cubic patches across the adjacent slices of those candidates. It is shown that the convolutional neural network cascade based detection approach outperforms the current top-performing neuron termination detection methods in many challenging datasets.