Reconstruction, Techniques and Validation
Reconstruction algorithms are used to build a geometric model of neurons, describing their morphology, from two-dimensional and three-dimensional images. Validation methods are used to determine the accuracy of the resulting models. Both reconstruction and validation are active areas of research, where proposed algorithms must address several complex problems, including (a) the use of a wide range of imaging modalities used to collect data, (b) the complex topological structure of interconnected branches within the neurons, and (c) automation for large data sets. Several methods have been proposed for addressing these issues; however, current reconstruction algorithms can be broadly placed into four categories: semiautomated software packages, local exploration, global processing, and crowdsourcing-based approaches.
Neuronal reconstructions provide geometric representations of cell and network morphology that can be used to perform quantitative analysis...
- Ascoli GA, Krichmar JL, Nasuto SJ, Senft SL (2001) Generation, description and storage of dendritic morphology data. Philos Trans R Soc Lond B 356(1412):1131–1145Google Scholar
- Bower JM, Beeman D (1998) The book of GENESIS: exploring realistic neural models with the GEneral NEural SImulation System. Springer, New YorkGoogle Scholar
- Carnevale NT, Hines ML (2009) The neuron book. Cambridge University Press, CambridgeGoogle Scholar
- Eberhard J, Wanner A, Wittum G (2006) NeuGen: a tool for the generation of realistic morphology of cortical neurons and neural networks in 3D. Neurocomputing 70(3):327–342Google Scholar
- Jain V, Bollmann B, Richardson M, Berger D, Helmstaedter M, Briggman K, Denk W, Bowden J, Mendenhall J, Abraham W, Harris K, Kasthuri N, Hayworth K, Schalek R, Tapia JC, Lichtman J, Seung HS (2010a). Boundary learning by optimization with topological constraints. In: IEEE conference on computer vision and pattern recognition, San Francisco, CA, pp 2488–2495Google Scholar
- Jain V, Seung SH, Turaga SC (2010b) Machines that learn to segment images: a crucial technology for connectomics. Curr Opin Neurobiol 20:1–14Google Scholar
- Luebke D, Reddy M, Cohen J, Varshney A, Watson B, Huebner R (2002) Level of detail for 3D graphics. Morgan Kaufmann, Palo AltoGoogle Scholar
- Meijering E (2010) Neuron tracing in perspective. Cytometry A 77A:693–704Google Scholar
- Meila M (2003) Comparing clusterings by the variation of information. Learning theory and Kernel machines. Lect Notes Comput Sci 2777:173–187Google Scholar
- Seung S, Burnes L (2012) Eyewire. http://eyewire.org/