Electron microscopy reconstruction of brain structure using sparse representations over learned dictionaries

Tao Hu, Juan Nunez-Iglesias, Shiv Vitaladevuni, Lou Scheffer, Shan Xu, Mehdi Bolorizadeh, Harald Hess, Richard Fetter, Dmitri B. Chklovskii

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)

Abstract

A central problem in neuroscience is reconstructing neuronal circuits on the synapse level. Due to a wide range of scales in brain architecture such reconstruction requires imaging that is both high-resolution and high-throughput. Existing electron microscopy (EM) techniques possess required resolution in the lateral plane and either high-throughput or high depth resolution but not both. Here, we exploit recent advances in unsupervised learning and signal processing to obtain high depth-resolution EM images computationally without sacrificing throughput. First, we show that the brain tissue can be represented as a sparse linear combination of localized basis functions that are learned using high-resolution datasets. We then develop compressive sensing-inspired techniques that can reconstruct the brain tissue from very few (typically five) tomographic views of each section. This enables tracing of neuronal processes and, hence, high throughput reconstruction of neural circuits on the level of individual synapses.

Original languageEnglish
Article number6573368
Pages (from-to)2179-2188
Number of pages10
JournalIEEE Transactions on Medical Imaging
Volume32
Issue number12
DOIs
Publication statusPublished - 1 Dec 2013
Externally publishedYes

Keywords

  • Dictionary learning
  • Electron microscopy
  • Neuronal circuitry
  • Sparse representation
  • Super-resolution

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