@article {6676, title = {Electron microscopy reconstruction of brain structure using sparse representations over learned dictionaries.}, journal = {IEEE Transactions on Medical Imaging}, volume = {32}, year = {2013}, month = {2013 Aug 2}, pages = {2179-88}, 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.

}, issn = {1558-254X}, doi = {10.1109/TMI.2013.2276018}, author = {Hu, Tao and Nunez-Iglesias, Juan and Vitaladevuni, Shiv and Scheffer, Lou and Xu, Shan and Bolorizadeh, Mehdi and Hess, Harald and Fetter, Richard and Chklovskii, Dmitri} } @article {4325, title = {High resolution segmentation of neuronal tissues from low depth-resolution EM imagery.}, journal = {8th International Conference of Energy Minimization Methods in Computer Vision and Pattern Recognition Energy Minimization Methods in Computer Vision and Pattern Recognition}, volume = {6819}, year = {2011}, month = {2011}, pages = {261-72}, abstract = {

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.

}, issn = {978-3-642-23093-6}, author = {Glasner, Daniel and Hu, Tao and Nunez-Iglesias, Juan and Scheffer, Lou and Xu, CS and Hess, Harald and Fetter, Richard and Chklovskii, Dmitri and Basri, Ronen} } @article {4327, title = {Wiring economy and volume exclusion determine neuronal placement in the Drosophila brain.}, journal = {Current Biology}, volume = {21}, year = {2011}, month = {2011 Dec}, pages = {2000-5}, abstract = {

Wiring economy has successfully explained the individual placement of neurons in simple nervous systems like that of Caenorhabditis elegans [1-3] and the locations of coarser structures like cortical areas in complex vertebrate brains [4]. However, it remains unclear whether wiring economy can explain the placement of individual neurons in brains larger than that of C.\ elegans. Indeed, given the greater number of neuronal interconnections in larger brains, simply minimizing the length of connections results in unrealistic configurations, with multiple neurons occupying the same position in space. Avoiding such configurations, or volume exclusion, repels neurons from each other, thus counteracting wiring economy. Here we test whether wiring economy together with volume exclusion can explain the placement of neurons in a module of the Drosophila melanogaster brain known as lamina cartridge [5-13]. We used newly developed techniques for semiautomated reconstruction from serial electron microscopy (EM) [14] to obtain the shapes of neurons, the location of synapses, and the resultant synaptic connectivity. We show that wiring length minimization and volume exclusion together can explain the structure of the lamina microcircuit. Therefore, even in brains larger than that of C.\ elegans, at least for some circuits, optimization can play an important role in individual neuron placement.

}, issn = {1879-0445}, doi = {10.1016/j.cub.2011.10.022}, author = {Rivera-Alba, Marta and Vitaladevuni, Shiv N. and Mischenko, Yuriy and Lu, Zhiyuan and Takemura, Shin-ya and Scheffer, Lou and Meinertzhagen, Ian and Chklovskii, Dmitri and Polavieja, Gonzalo} } @article {1245, title = {Increasing depth resolution of electron microscopy of neural circuits using sparse tomographic reconstruction.}, journal = {Computer Vision and Pattern Recognition (CVPR)}, year = {2010}, month = {2010}, pages = {1767-74}, issn = {1063-6919}, doi = {10.1109/CVPR.2010.5539846}, author = {Veeraraghavan, Ashok and Genkin, Alex V and Vitaladevuni, Shiv and Scheffer, Lou and Xu, CShan and Hess, Harald and Fetter, Richard and Cantoni, Marco and Knott, Graham and Chklovskii, Dmitri B} }