Morphological change tracking of dendritic spines based on structural features

Abstract

SummaryIdentification and tracking of dendritic spine morphology from two-dimensional time-lapsed images plays an important role in neurobiological research. Such analysis can enable us to derive a correlation between morphological characteristics and molecular mechanism of dendritic spine development and remodelling. Moreover, Neuronal morphology of hippocampal Cornu Ammonis 1 region is critical for understanding the Alzheimer's disease. Therefore, we need to extract and trace the dendritic spines accurately for examining their development and remodelling processes, which are related to functions of hippocampal Cornu Ammonis 1. There are some problems to be solved in related researches. Noise due to the properties of optical microscopes makes it difficult to identify and trace dendritic spines accurately. To solve these problems, in this paper we present a local spine detection technique minimizing noise influence in two-dimensional optical microscopy images. Also, we suggest an efficient mapping method for tracking the dynamics of dendritic spines to measure their morphological changes quantitatively. First, to utilize structural feature of spines, which are small protrusions of tree-like dendrites, we extract the tips of each dendritic branch and use this position as an initial contour position for a deformable model-based segmentation. We then use a geodesic active contour model to detect the spines accurately. Secondly, we apply an optical flow method, which takes into account both structure and movement of objects, to map every time-series image frame. Proposed method provides accurate measurements of dendritic spine length, volume, shape classification for time-lapse images of dendrites of hippocampal neurons. We compared the proposed Correspondence to: Myoung-Hee Kim, Medical

On comparison of manifold learning techniques for dendritic spine classification

“…They considered 3D confocal laser scanning microscopy (CLSM) images. Son et al [8] and Shi et al [9] also used morphological features and proposed a decision tree based classification system for CLSM images.…”

Methods of dendritic spine detection: From Golgi to high-resolution optical imaging

“…The sensitivity and specificity of the method were 95.3% and 90.1%, respectively. The work by (Son et al, 2011) used a deformable model to segment each spine with the initial points as the tip region of each spine that can be determined from the skeleton structure. Although implemented for 2D projection images the curve-skeletons obtained from Janoos et al’s work perfectly feed to this deformable segmentation.…”

“…Manual spine classification or computer assisted manual spine classification, long the standard approaches, are extremely labor intensive and unacceptably slow as well as subject to investigator variability and poor for 3D image analysis. Recently, a great deal of progress has been made toward the development of software tools that can accurately analyze dendritic structure and detect all classes of dendritic spines reproducibly using objective criteria (Rodriguez et al, 2006, Rodriguez et al, 2008, Fan et al, 2009, Li et al, 2010, Zhang et al, 2010, Son et al, 2011). Although to the human eye, the criteria that determine just what is a dendritic spine seem obvious, defining these criteria in an objective manner has been a challenge in algorithm development.…”

Merging advanced technologies with classical methods to uncover dendritic spine dynamics: A hot spot of synaptic plasticity

“…Furthermore, the manual or computer-assisted semi-automatic methods of spine classification used in CLSM are laborious, time-consuming, error-prone, and lack accurate 3D analysis. The use of new algorithmic software tools has allowed for the automated analysis of dendritic spine structure in CLSM (Rodriguez et al, 2006(Rodriguez et al, , 2008Fan et al, 2009;Zhang et al, 2010;Li et al, 2011;Son et al, 2011). These software packages provide high-resolution spine architecture with faster speed, and the acquiring images are more precise and accurate (Wearne et al, 2005;Rodriguez et al, 2006Rodriguez et al, , 2008.…”