Unsupervised Neural Tracing In Densely Labeled Multispectral Brainbow Images

Recent advances in imaging technologies for generating large quantities of high-resolution 3D images, especially multispectral labeling technology such as Brainbow, permits unambiguous differentiation of neighboring neurons in a densely labeled brain. This enables, for the first time, the possibility of studying the connectivity between many neurons from a light microscopy image. The lack of reliable automated neuron morphology reconstruction, however, makes data analysis the bottleneck of extracting rich informatics in neuroscience. Supervoxel-based neuron segmentation methods have been proposed to solve this problem, however, previous approaches have been impeded by the large numbers of errors which arise in the final segmentation. In this paper, we present a novel unsupervised approach to trace neurons from multispectral Brainbow images, which prevents segmentation errors and tracing continuity errors using two innovations: First, we formulate a Gaussian mixture model-based clustering strategy to improve the separation of segmented color channels that provides accurate skeletons for the next steps. Then, a skeleton graph approach is proposed to allow the identification and correction of discontinuities in the neuron tree topology. We find that these innovations allow better performance over current state-of-the-art approaches, which results in more accurate neuron tracing results close to human expert annotation.