Online three-dimensional dendritic spines mophological classification based on semi-supervised learning

Peng Shi, Xiaobo Zhou, Qing Li, Matthew Baron, Merilee A. Teylan, Yong Kim, Stephen T.C. Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Recent studies on neuron imaging show that there is a strong relationship between the functional properties of a neuron and its morphology, especially its dendritic spine structures. However, most of the current methods for morphological spine classification only concern features in two-dimensional (2D) space, which consequently decreases the accuracy of dendritic spine analysis. In this paper, we propose a semi-supervised learning (SSL) framework, in which spine phenotypes in three-dimensional (3D) space are considered. With training only on a few pre-classified inputs, the rest of the spines can be identified effectively. We also derived a new scheme using an affinity matrix between features to further improve the accuracy. Our experimental results indicate that a small training dataset is sufficient to classify detected dendritic spines.

Original languageEnglish (US)
Title of host publicationProceedings - 2009 IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2009
Pages1019-1022
Number of pages4
DOIs
StatePublished - 2009
Event2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 - Boston, MA, United States
Duration: Jun 28 2009Jul 1 2009

Publication series

NameProceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009

Other

Other2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009
CountryUnited States
CityBoston, MA
Period6/28/097/1/09

Keywords

  • Dendritic spine
  • Morphological spine classification
  • Semi-supervised learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'Online three-dimensional dendritic spines mophological classification based on semi-supervised learning'. Together they form a unique fingerprint.

Cite this