title: Optree: a learning-based adaptive watershed algorithm for neuron segmentation.
abstract: We present a new algorithm for automatic and interactive segmentation of neuron structures from electron microscopy (EM) images. Our method selects a collection of nodes from the watershed mergng tree as the proposed segmentation. This is achieved by building a onditional random field (CRF) whose underlying graph is the merging tree. The maximum a posteriori (MAP) prediction of the CRF is the output segmentation. Our algorithm outperforms state-of-the-art methods. Both the inference and the training are very efficient as the graph is tree-structured. Furthermore, we develop an interactive segmentation framework which selects uncertain regions for a user to proofread. The uncertainty is measured by the marginals of the graphical model. Based on user corrections, our framework modifies the merging tree and thus improves the segmentation globally.
authors.forename: Mustafa Gökhan
authors.initials: MG
authors.lastname: Uzunbaş
authors.forename: Chao
authors.initials: C
authors.lastname: Chen
authors.forename: Dimitris
authors.initials: D
authors.lastname: Metaxas
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002478:Cells, Cultured; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008854:Microscopy, Electron / Q000379:methods*; D009474:Neurons / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity