Online Multi-spectral Neuron Tracing
This work addresses the challenge of neuron tracing for researchers in neuroscience by offering a more accessible and efficient alternative to training-dependent methods, though it appears incremental in its approach.
The paper tackles the problem of neuron tracing in multi-spectral images by proposing an online method that eliminates the need for offline training and annotations, resulting in fast and accurate reconstructions with reduced user configuration effort.
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.