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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 5, Iss. 5 — May. 1, 2014
  • pp: 1541–1553

Automated three-dimensional reconstruction and morphological analysis of dendritic spines based on semi-supervised learning

Peng Shi, Yue Huang, and Jinsheng Hong  »View Author Affiliations

Biomedical Optics Express, Vol. 5, Issue 5, pp. 1541-1553 (2014)

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A dendritic spine is a small membranous protrusion from a neuron's dendrite that typically receives input from a single synapse of an axon. Recent research shows that the morphological changes of dendritic spines have a close relationship with some specific diseases. The distribution of different dendritic spine phenotypes is a key indicator of such changes. Therefore, it is necessary to classify detected spines with different phenotypes online. Since the dendritic spines have complex three dimensional (3D) structures, current neuron morphological analysis approaches cannot classify the dendritic spines accurately with limited features. In this paper, we propose a novel semi-supervised learning approach in order to perform the online morphological classification of dendritic spines. Spines are detected by a new approach based on wavelet transform in the 3D space. A small training data set is chosen from the detected spines, which has the spines labeled by the neurobiologists. The remaining spines are then classified online by the semi-supervised learning (SSL) approach. Experimental results show that our method can quickly and accurately analyze neuron images with modest human intervention.

© 2014 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(100.5010) Image processing : Pattern recognition
(100.6890) Image processing : Three-dimensional image processing

ToC Category:
Image Processing

Original Manuscript: February 18, 2014
Revised Manuscript: April 10, 2014
Manuscript Accepted: April 10, 2014
Published: April 17, 2014

Peng Shi, Yue Huang, and Jinsheng Hong, "Automated three-dimensional reconstruction and morphological analysis of dendritic spines based on semi-supervised learning," Biomed. Opt. Express 5, 1541-1553 (2014)

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