Online Multi-spectral Neuron Tracing
Bin Duan Yuzhang Shang Dawen Cai Yan Yan
In this paper, we propose an online multi-spectral neuron tracing method with
uniquely designed modules, where no offline training are required. Our method
is trained online to update our enhanced discriminative correlation filter to
conglutinate the tracing process. This distinctive offline-training-free schema
differentiates us from other training-dependent tracing approaches like deep
learning methods since no annotation is needed for our method. Besides,
compared to other tracing methods requiring complicated set-up such as for
clustering and graph multi-cut, our approach is much easier to be applied to
new images. In fact, it only needs a starting bounding box of the tracing
neuron, significantly reducing users' configuration effort. Our extensive
experiments show that our training-free and easy-configured methodology allows
fast and accurate neuron reconstructions in multi-spectral images.