title: WATERSHED MERGE FOREST CLASSIFICATION FOR ELECTRON MICROSCOPY IMAGE STACK SEGMENTATION.
abstract: Automated electron microscopy (EM) image analysis techniques can be tremendously helpful for connectomics research. In this paper, we extend our previous work [1] and propose a fully automatic method to utilize inter-section information for intra-section neuron segmentation of EM image stacks. A watershed merge forest is built via the watershed transform with each tree representing the region merging hierarchy of one 2D section in the stack. A section classifier is learned to identify the most likely region correspondence between adjacent sections. The inter-section information from such correspondence is incorporated to update the potentials of tree nodes. We resolve the merge forest using these potentials together with consistency constraints to acquire the final segmentation of the whole stack. We demonstrate that our method leads to notable segmentation accuracy improvement by experimenting with two types of EM image data sets.
authors.affiliation: Scientific Computing and Imaging Institute, University of Utah.
authors.forename: Ting
authors.initials: T
authors.lastname: Liu
authors.affiliation: Scientific Computing and Imaging Institute, University of Utah.
authors.forename: Mojtaba
authors.initials: M
authors.lastname: Seyedhosseini
authors.affiliation: National Center for Microscopy and Imaging Research, University of California, San Diego.
authors.forename: Mark
authors.initials: M
authors.lastname: Ellisman
authors.affiliation: Scientific Computing and Imaging Institute, University of Utah.
authors.forename: Tolga
authors.initials: T
authors.lastname: Tasdizen
keywords: Machine learning; neural circuit reconstruction; neuron segmentation; random forest; watershed