@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 = {<p>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 <a class="reference-link webtrekk-track" href="http://link.springer.com/search?dc.title=Ion+Beam\&amp;facet-content-type=ReferenceWorkEntry\&amp;sortOrder=relevance">Ion Beam</a> (FIB) images and on low depth-resolution images acquired with ssTEM and evaluate our results by comparing it to manual labeling of this data.</p>
},
	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}
}
