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

doi:10.1109/ISBI.2009.5193228

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 proceeding › Conference contribution

9 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 language	English (US)
Title of host publication	Proceedings - 2009 IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro, ISBI 2009
Pages	1019-1022
Number of pages	4
DOIs	https://doi.org/10.1109/ISBI.2009.5193228
State	Published - 2009
Event	2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 - Boston, MA, United States Duration: Jun 28 2009 → Jul 1 2009

Publication series

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

Other

Other	2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009
Country/Territory	United States
City	Boston, MA
Period	6/28/09 → 7/1/09

Keywords

Dendritic spine
Morphological spine classification
Semi-supervised learning

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/ISBI.2009.5193228

Cite this

Shi, P., Zhou, X., Li, Q., Baron, M., Teylan, M. A., Kim, Y., & Wong, S. T. C. (2009). Online three-dimensional dendritic spines mophological classification based on semi-supervised learning. In Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 (pp. 1019-1022). Article 5193228 (Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009). https://doi.org/10.1109/ISBI.2009.5193228

Online three-dimensional dendritic spines mophological classification based on semi-supervised learning. / Shi, Peng; Zhou, Xiaobo; Li, Qing et al.
Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009. 2009. p. 1019-1022 5193228 (Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Shi, P, Zhou, X, Li, Q, Baron, M, Teylan, MA, Kim, Y & Wong, STC 2009, Online three-dimensional dendritic spines mophological classification based on semi-supervised learning. in Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009., 5193228, Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, pp. 1019-1022, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, Boston, MA, United States, 6/28/09. https://doi.org/10.1109/ISBI.2009.5193228

Shi P, Zhou X, Li Q, Baron M, Teylan MA, Kim Y et al. Online three-dimensional dendritic spines mophological classification based on semi-supervised learning. In Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009. 2009. p. 1019-1022. 5193228. (Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009). doi: 10.1109/ISBI.2009.5193228

Shi, Peng ; Zhou, Xiaobo ; Li, Qing et al. / Online three-dimensional dendritic spines mophological classification based on semi-supervised learning. Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009. 2009. pp. 1019-1022 (Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009).

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title = "Online three-dimensional dendritic spines mophological classification based on semi-supervised learning",

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.",

keywords = "Dendritic spine, Morphological spine classification, Semi-supervised learning",

author = "Peng Shi and Xiaobo Zhou and Qing Li and Matthew Baron and Teylan, {Merilee A.} and Yong Kim and Wong, {Stephen T.C.}",

note = "Copyright: Copyright 2009 Elsevier B.V., All rights reserved.; 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 ; Conference date: 28-06-2009 Through 01-07-2009",

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AU - Kim, Yong

AU - Wong, Stephen T.C.

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AB - 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.

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

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