title: An efficient conditional random field approach for automatic and interactive neuron segmentation.
abstract: 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.
authors.affiliation: Department of Computer Science, Rutgers University, 110 Frelinghuysen Road, Piscataway, NJ 08854-8019, USA. Electronic address: uzunbas@cs.rutgers.edu.
authors.forename: Mustafa Gokhan
authors.initials: MG
authors.lastname: Uzunbas
authors.affiliation: Department of Computer Science, Rutgers University, 110 Frelinghuysen Road, Piscataway, NJ 08854-8019, USA. Electronic address: chao.chen.cchen@gmail.com.
authors.forename: Chao
authors.initials: C
authors.lastname: Chen
authors.affiliation: Department of Computer Science, Rutgers University, 110 Frelinghuysen Road, Piscataway, NJ 08854-8019, USA. Electronic address: dnm@cs.rutgers.edu.
authors.forename: Dimitris
authors.initials: D
authors.lastname: Metaxas
mesh_terms: D000465:Algorithms; D000818:Animals; D058948:Cell Tracking / Q000379:methods; D003196:Computer Graphics; D003198:Computer Simulation; D004331:Drosophila melanogaster; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D051379:Mice; D008854:Microscopy, Electron / Q000379:methods*; D015233:Models, Statistical; D009474:Neurons / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique
keywords: Conditional random field; EM segmentation; User interaction; Watershed