Shi Peng, Huang Yue, Hong Jinsheng
School of Mathematics and Computer Science, Fujian Normal University, Fuzhou, Fujian 350180, China.
Department of Automation, Xiamen University, Xiamen, Fujian 361005, China.
Biomed Opt Express. 2014 Apr 17;5(5):1541-53. doi: 10.1364/BOE.5.001541. eCollection 2014 May 1.
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.
树突棘是神经元树突上的一种小的膜状突起，通常接收来自轴突单个突触的输入。最近的研究表明，树突棘的形态变化与某些特定疾病密切相关。不同树突棘表型的分布是此类变化的关键指标。因此，有必要对检测到的不同表型的棘进行在线分类。由于树突棘具有复杂的三维（3D）结构，当前的神经元形态分析方法无法利用有限的特征准确地对树突棘进行分类。在本文中，我们提出了一种新颖的半监督学习方法，以便对树突棘进行在线形态分类。通过一种基于3D空间小波变换的新方法检测棘。从检测到的棘中选择一个小的训练数据集，其中的棘由神经生物学家标记。然后，其余的棘通过半监督学习（SSL）方法进行在线分类。实验结果表明，我们的方法在适度的人工干预下能够快速准确地分析神经元图像。
