title: Anisotropic ssTEM image segmentation using dense correspondence across sections.
abstract: Connectomics based on high resolution ssTEM imagery requires reconstruction of the neuron geometry from histological slides. We present an approach for the automatic membrane segmentation in anisotropic stacks of electron microscopy brain tissue sections. The ambiguities in neuronal segmentation of a section are resolved by using the context from the neighboring sections. We find the global dense correspondence between the sections by SIFT Flow algorithm, evaluate the features of the corresponding pixels and use them to perform the segmentation. Our method is 3.6 and 6.4% more accurate in two different accuracy metrics than the algorithm with no context from other sections.
authors.affiliation: Department of Computer Science, ETH Zurich, Switzerland. dlaptev@inf.ethz.ch
authors.forename: Dmitry
authors.initials: D
authors.lastname: Laptev
authors.forename: Alexander
authors.initials: A
authors.lastname: Vezhnevets
authors.forename: Sarvesh
authors.initials: S
authors.lastname: Dwivedi
authors.forename: Joachim M
authors.initials: JM
authors.lastname: Buhmann
mesh_terms: D000465:Algorithms; D016880:Anisotropy; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods; D003952:Diagnostic Imaging / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008854:Microscopy, Electron / Q000379:methods; D046529:Microscopy, Electron, Transmission / Q000379:methods*; D009445:Neuroanatomy / Q000379:methods; D015203:Reproducibility of Results