High resolution segmentation of neuronal tissues from low depth-resolution em imagery

Daniel Glasner, Tao Hu, Juan Nunez-Iglesias, Lou Scheffer, Shan Xu, Harald Hess, Richard Fetter, Dmitri Chklovskii, Ronen Basri

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)


The challenge of recovering the topology of massive neuronal circuits can potentially be met by high throughput Electron Microscopy (EM) imagery. Segmenting a 3-dimensional stack of EM images into the individual neurons is difficult, due to the low depth-resolution in existing high-throughput EM technology, such as serial section Transmission EM (ssTEM). In this paper we propose methods for detecting the high resolution locations of membranes from low depth-resolution images. We approach this problem using both a method that learns a discriminative, over-complete dictionary and a kernel SVM. We test this approach on tomographic sections produced in simulations from high resolution Focused Ion Beam (FIB) images and on low depth-resolution images acquired with ssTEM and evaluate our results by comparing it to manual labeling of this data.

Original languageEnglish
Title of host publicationEnergy Minimazation Methods in Computer Vision and Pattern Recognition - 8th International Conference, EMMCVPR 2011, Proceedings
Number of pages12
Publication statusPublished - 22 Aug 2011
Externally publishedYes
EventInternational Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR) 2011 - St. Petersburg, Russian Federation
Duration: 25 Jul 201127 Jul 2011
Conference number: 8th
https://link.springer.com/book/10.1007/978-3-642-23094-3 (Proceedings)

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6819 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR) 2011
Abbreviated titleEMMCVPR 2011
Country/TerritoryRussian Federation
CitySt. Petersburg
Internet address


  • Connectomics
  • Segmentation of neuronal tissues
  • Sparse over-complete representation
  • Task-driven dictionary learning

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