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.
authors.affiliation: Center for Biotechnology and Informatics, The Methodist Hospital Research Institute, and Department of Radiology, The Methodist Hospital, Weill Cornell Medical College, Houston, TX 77030, USA.
authors.forename: Peng
authors.initials: P
authors.lastname: Shi
authors.forename: Xiaobo
authors.initials: X
authors.lastname: Zhou
authors.forename: Qing
authors.initials: Q
authors.lastname: Li
authors.forename: Matthew
authors.initials: M
authors.lastname: Baron
authors.forename: Merilee A
authors.initials: MA
authors.lastname: Teylan
authors.forename: Yong
authors.initials: Y
authors.lastname: Kim
authors.forename: Stephen T C
authors.initials: ST
authors.lastname: Wong