Multiple Structure Tracing in 3D Electron Micrographs

Automatic interpretation of Transmission Electron Micrograph (TEM) volumes is central to advancing current understanding of neural circuitry. In the context of TEM image analysis, tracing 3D neuronal structures is a significant problem. This work proposes a new model using the conditional random field (CRF) framework with higher order potentials for tracing multiple neuronal structures in 3D. The model consists of two key features. First, the higher order CRF cost is designed to enforce label smoothness in 3D and capture rich textures inherent in the data. Second, a technique based on semi-supervised edge learning is used to propagate high confidence structural edges during the tracing process. In contrast to predominantly edge based methods in the TEM tracing literature, this work simultaneously combines regional texture and learnt edge features into a single framework. Experimental results show that the proposed method outperforms more traditional models in tracing neuronal structures from TEM stacks.