title: A Fast Method for the Segmentation of Synaptic Junctions and Mitochondria in Serial Electron Microscopic Images of the Brain.
abstract: Recent electron microscopy (EM) imaging techniques permit the automatic acquisition of a large number of serial sections from brain samples. Manual segmentation of these images is tedious, time-consuming and requires a high degree of user expertise. Therefore, there is considerable interest in developing automatic segmentation methods. However, currently available methods are computationally demanding in terms of computer time and memory usage, and to work properly many of them require image stacks to be isotropic, that is, voxels must have the same size in the X, Y and Z axes. We present a method that works with anisotropic voxels and that is computationally efficient allowing the segmentation of large image stacks. Our approach involves anisotropy-aware regularization via conditional random field inference and surface smoothing techniques to improve the segmentation and visualization. We have focused on the segmentation of mitochondria and synaptic junctions in EM stacks from the cerebral cortex, and have compared the results to those obtained by other methods. Our method is faster than other methods with similar segmentation results. Our image regularization procedure introduces high-level knowledge about the structure of labels. We have also reduced memory requirements with the introduction of energy optimization in overlapping partitions, which permits the regularization of very large image stacks. Finally, the surface smoothing step improves the appearance of three-dimensional renderings of the segmented volumes.
authors.affiliation: Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 28660, Boadilla del Monte, Madrid, Spain.
authors.forename: Pablo
authors.initials: P
authors.lastname: Márquez Neila
authors.affiliation: Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 28660, Boadilla del Monte, Madrid, Spain.
authors.forename: Luis
authors.initials: L
authors.lastname: Baumela
authors.affiliation: Departamento de Anatomía. Facultad de Veterinaria, Universidad Complutense, Madrid, Spain.
authors.forename: Juncal
authors.initials: J
authors.lastname: González-Soriano
authors.affiliation: Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid and Instituto Cajal, CSIC, Madrid, Spain.
authors.forename: Jose-Rodrigo
authors.initials: JR
authors.lastname: Rodríguez
authors.affiliation: Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid and Instituto Cajal, CSIC, Madrid, Spain.
authors.forename: Javier
authors.initials: J
authors.lastname: DeFelipe
authors.affiliation: Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid and Instituto Cajal, CSIC, Madrid, Spain. amerchan@fi.upm.es.|Departamento de Arquitectura y Tecnología de Sistemas Informáticos, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain. amerchan@fi.upm.es.
authors.forename: Ángel
authors.initials: Á
authors.lastname: Merchán-Pérez
mesh_terms: D000465:Algorithms; D000818:Animals; D002540:Cerebral Cortex / Q000166:cytology* / Q000648:ultrastructure; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008854:Microscopy, Electron; D008928:Mitochondria / Q000648:ultrastructure*; D009474:Neurons / Q000648:ultrastructure*; D013569:Synapses / Q000648:ultrastructure*
keywords: Automatic image segmentation; Mitochondria; Synapses; Three-dimensional electron microscopy; cerebral cortex