An efficient conditional random field approach for automatic and interactive neuron segmentation
We present a new graphical model based method for automatic and interactive segmentation of neuron structures from electron microscopy (EM) images. For automated reconstruction, our learning based model selects a collection of nodes from a hierarchical merging tree as the proposed segmentation. More specifically, this is achieved by training a conditional random field (CRF) whose underlying graph is the watershed merging tree. The maximum a posteriori (MAP) prediction of the CRF is the output segmentation. Our results are comparable to the results of state of the art methods. Furthermore, both the inference and the training are very efficient as the graph is tree structured. The problem of neuron segmentation requires extremely high segmentation quality. Therefore, proofreading, namely, interactively correcting mistakes of the automatic method, is a necessary module in the pipeline. Based on our efficient tree structured inference algorithm, we develop an interactive segmentation framework which only selects locations where the model is uncertain for a user to proofread. The uncertainty is measured by the marginals of the graphical model. Only giving a limited number of choices makes the user interaction very efficient. Based on user corrections, our framework modifies the merging tree and thus improves the segmentation globally.
Medical Image Anal.