Choy Siu-Kai, Chen Kun, Zhang Yong, Baron Matthew, Teylan Merilee A, Kim Yong, Tong Chong-Sze, Song Zhihuan, Wong Stephen T C
Department of Mathematics, Hong Kong Baptist University, Hong Kong, China.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:4765-8. doi: 10.1109/IEMBS.2010.5626640.
Dendritic spines play an essential role in the central nervous system. Recent experiments have revealed that neuron functional properties are highly correlated with the statistical and morphological changes of the dendritic spines. In this paper, we propose a new multi scale approach for detecting dendritic spines in a 2D Maximum Intensity Projection (MIP) image of the 3D neuron data stacks collected from a 2-photon laser scanning confocal microscope. The proposed method utilizes the curvilinear structure detector in conjunction with the multi scale spine detection algorithm which automatically and accurately extracts and segments the spines with variational sizes along the dendrite. In addition, a slice-based spine detection algorithm is also proposed to detect spines which are hidden from the MIP image within the dendrite area. Experimental results show that our proposed method is effective for automatic spine detection and is able to accurately segment dendrite.
树突棘在中枢神经系统中起着至关重要的作用。最近的实验表明，神经元的功能特性与树突棘的统计和形态变化高度相关。在本文中，我们提出了一种新的多尺度方法，用于在从双光子激光扫描共聚焦显微镜收集的三维神经元数据堆栈的二维最大强度投影（MIP）图像中检测树突棘。所提出的方法将曲线结构检测器与多尺度棘检测算法结合使用，该算法能自动且准确地沿着树突提取和分割不同大小的棘。此外，还提出了一种基于切片的棘检测算法，以检测在树突区域内MIP图像中隐藏的棘。实验结果表明，我们提出的方法对于自动棘检测是有效的，并且能够准确地分割树突。
