Automatic Reconstruction of Dendrite Morphology from Optical Section Stacks
The function of the human brain arises from computations that occur within and among billions of nerve cells known as neurons. A neuron is composed primarily of a cell body (soma) from which emanates a collection of branching structures (dendrites). How neuronal signals are processed is dependent on the dendrites’ specific morphology and distribution of voltage-gated ion channels. To understand this processing, it is necessary to acquire an accurate structural analysis of the cell. Toward this end, we present an automated reconstruction system which extracts the morphology of neurons imaged from confocal and multi-photon microscopes. As we place emphasis on this being a rapid (and therefore automated) process, we have developed several techniques that provide high-quality reconstructions with minimal human interaction. In addition to generating a tree of connected cylinders representing the reconstructed neuron, a computational model is also created for purposes of performing functional simulations. We present visual and statistical results from reconstructions performed both on real image volumes and on noised synthetic data from the Duke-Southampton archive.
KeywordsPoint Spread Function Medial Axis Dendrite Morphology Distance Transform Parseval Frame
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- 1.Urban, S.: Automatic reconstruction of dendrite morphologies from optical sections of living fluorescently-labeled neurons. Master’s thesis, Univ. of Houston (2005)Google Scholar
- 2.NEURON (2005), http://www.neuron.yale.edu/neuron
- 5.Uehara, C., Colbert, C., Saggau, P., Kakadiaris, I.: Towards automatic reconstruction of dendrite morphology from live neurons. In: Proc. IEEE EMBS (2004)Google Scholar
- 9.Konstantinidis, I., Santamaría-Pang, A., Kakadiaris, I.A.: Frames-based denoising in 3D confocal microscopy imaging. In: Proc. IEEE EMBS (2005)Google Scholar
- 10.Santamaría-Pang, A., Bildea, T.S., Konstantinidis, I., Kakadiaris, I.A.: Adaptive frames-based denoising of confocal microscopy data. In: Proc. IEEE Intl. Conf. Acoust Speech (ICASSP) (2006)Google Scholar
- 14.Rall, W.: Core conductor theory and cable properties of neurons. In: Handbook of Physiology: The Nervous System, vol. 1, pp. 39–98. Williams and Wilkins (1977)Google Scholar
- 16.Duke-Southampton Archive (2005), http://neuron.duke.edu/cells/