@article {46325,
	title = {Ultrastructurally smooth thick partitioning and volume stitching for large-scale connectomics.},
	journal = {Nature Methods},
	volume = {12},
	year = {2015},
	month = {2015 Feb 16},
	pages = {319-22},
	abstract = {<p>Focused-ion-beam scanning electron microscopy (FIB-SEM) has become an essential tool for studying neural tissue at resolutions below 10 nm {\texttimes} 10 nm {\texttimes} 10 nm, producing data sets optimized for automatic connectome tracing. We present a technical advance, ultrathick sectioning, which reliably subdivides embedded tissue samples into chunks (20 μm thick) optimally sized and mounted for efficient, parallel FIB-SEM imaging. These chunks are imaged separately and then {\textquoteright}volume stitched{\textquoteright} back together, producing a final three-dimensional data set suitable for connectome tracing.</p>
},
	issn = {1548-7105},
	doi = {10.1038/nmeth.3292},
	author = {Hayworth, Kenneth J and Xu, C Shan and Lu, Zhiyuan and Knott, Graham W and Fetter, Richard D and Tapia, Juan Carlos and Lichtman, Jeff W and Hess, Harald F}
}
@conference {3858,
	title = {Boundary learning by optimization with topological constraints.},
	booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
	year = {2010},
	month = {2010},
	abstract = {<p>Recent studies have shown that machine learning can improve the accuracy of detecting object boundaries in images. In the standard approach, a boundary detector is trained by minimizing its pixel-level disagreement with human boundary tracings. This naive metric is problematic because it is overly sensitive to boundary locations. This problem is solved by metrics provided with the Berkeley Segmentation Dataset, but these can be insensitive to topological differences, such as gaps in boundaries. Furthermore, the Berkeley metrics have not been useful as cost functions for supervised learning. Using concepts from digital topology, we propose a new metric called the warping error that tolerates disagreements over boundary location, penalizes topological disagreements, and can be used directly as a cost function for learning boundary detection, in a method that we call Boundary Learning by Optimization with Topological Constraints (BLOTC). We trained boundary detectors on electron microscopic images of neurons, using both BLOTC and standard training. BLOTC produced substantially better performance on a 1.2 million pixel test set, as measured by both the warping error and the Rand index evaluated on segmentations generated from the boundary labelings. We also find our approach yields significantly better segmentation performance than either gPb-OWT-UCM or multiscale normalized cut, as well as Boosted Edge Learning trained directly on our data.</p>
},
	author = {Jain, Viren and Bollmann, Benjamin and Richardson, Mark and Berger, Daniel R. and Helmstaedter, Moritz N. and Briggman, Kevin L and Denk, Winfried and Bowden, Jared B. and Mendenhall, John M. and Abraham, Wickliffe C. and Harris, Kristen M and Kasthuri, Narayanan and Hayworth, Ken J. and Schalek, Richard and Tapia, Juan Carlos and Lichtman, Jeff W. and Seung, Hyunjune Sebastian}
}
