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

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IEEE Transactions on Medical Imaging. 2013 Aug 2;32(12):2179-88. doi: 10.1109/TMI.2013.2276018

Electron microscopy reconstruction of brain structure using sparse representations over learned dictionaries. Scheffer LabHess LabFetter Lab

Hu T, Nunez-Iglesias J, Vitaladevuni S, Scheffer L, Xu S, Bolorizadeh M, Hess H, Fetter R, Chklovskii D

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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 5) 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.

PMID: 23925366 [PubMed - indexed for MEDLINE]

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

Abstract

PMID: 23925366 [PubMed - indexed for MEDLINE]

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