@article {47159,
	title = {Crowdsourcing the creation of image segmentation algorithms for connectomics.},
	journal = {Frontiers in Neuroanatomy},
	volume = {9},
	year = {2015},
	month = {2015 Nov 05},
	pages = {142},
	abstract = {<p>To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This {\textquotedblleft}deep learning{\textquotedblright} approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge.</p>
},
	doi = {10.3389/fnana.2015.00142},
	author = {Arganda-Carreras, Ignacio and Turaga, Srinivas C and Berger, Daniel R and Ciresan, Dan and Giusti, Alessandro and Gambardella, Luca M and Schmidhuber, Juergen and Laptev, Dmitry and Dwivedi, Sarvesh and Buhmann, Joachim M}
}
@article {45032,
	title = {Imaging ATUM ultrathin section libraries with WaferMapper: a multi-scale approach to EM reconstruction of neural circuits.},
	journal = {Frontiers in Neural Circuits},
	volume = {8},
	year = {2014},
	month = {2014 Jun 27},
	pages = {68},
	abstract = {<p>The automated tape-collecting ultramicrotome (ATUM) makes it possible to collect large numbers of ultrathin sections quickly-the equivalent of a petabyte of high resolution images each day. However, even high throughput image acquisition strategies generate images far more slowly (at present ~1 terabyte per day). We therefore developed WaferMapper, a software package that takes a multi-resolution approach to mapping and imaging select regions within a library of ultrathin sections. This automated method selects and directs imaging of corresponding regions within each section of an ultrathin section library (UTSL) that may contain many thousands of sections. Using WaferMapper, it is possible to map thousands of tissue sections at low resolution and target multiple points of interest for high resolution imaging based on anatomical landmarks. The program can also be used to expand previously imaged regions, acquire data under different imaging conditions, or re-image after additional tissue treatments.</p>
},
	issn = {1662-5110},
	doi = {10.3389/fncir.2014.00068},
	author = {Hayworth, Kenneth J and Morgan, Josh L and Schalek, Richard and Berger, Daniel R and Hildebrand, David G C and Lichtman, Jeff W}
}
