The Ting Tsung and Wei Fong Chao Center for Bioinformatics Research and Neurosciences Imaging, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, TX 77030, USA.
Neuroimage. 2010 May 1;50(4):1472-84. doi: 10.1016/j.neuroimage.2010.01.048. Epub 2010 Jan 25.
Acquisition and quantitative analysis of high resolution images of dendritic spines are challenging tasks but are necessary for the study of animal models of neurological and psychiatric diseases. Currently available methods for automated dendritic spine detection are for the most part customized for 2D image slices, not volumetric 3D images. In this work, a fully automated method is proposed to detect and segment dendritic spines from 3D confocal microscopy images of medium-sized spiny neurons (MSNs). MSNs constitute a major neuronal population in striatum, and abnormalities in their function are associated with several neurological and psychiatric diseases. Such automated detection is critical for the development of new 3D neuronal assays which can be used for the screening of drugs and the studies of their therapeutic effects. The proposed method utilizes a generalized gradient vector flow (GGVF) with a new smoothing constraint and then detects feature points near the central regions of dendrites and spines. Then, the central regions are refined and separated based on eigen-analysis and multiple shape measurements. Finally, the spines are segmented in 3D space using the fast marching algorithm, taking the detected central regions of spines as initial points. The proposed method is compared with three popular existing methods for centerline extraction and also with manual results for dendritic spine detection in 3D space. The experimental results and comparisons show that the proposed method is able to automatically and accurately detect, segment, and quantitate dendritic spines in 3D images of MSNs.
获取和定量分析树突棘的高分辨率图像是具有挑战性的任务，但对于研究神经和精神疾病的动物模型是必要的。目前用于自动树突棘检测的方法在大多数情况下是针对 2D 图像切片定制的，而不是针对体积 3D 图像。在这项工作中，提出了一种全自动方法，用于从中型棘突神经元（MSNs）的 3D 共聚焦显微镜图像中检测和分割树突棘。MSNs 构成纹状体中的主要神经元群体，其功能异常与几种神经和精神疾病有关。这种自动检测对于开发新的 3D 神经元测定法至关重要，这些测定法可用于药物筛选和研究其治疗效果。所提出的方法利用具有新平滑约束的广义梯度矢量流（GGVF），然后检测树突和棘附近的特征点。然后，根据特征分析和多种形状测量对中心区域进行细化和分离。最后，使用快速行进算法在 3D 空间中分割棘，将检测到的棘的中心区域作为初始点。将所提出的方法与三种流行的现有中心线提取方法进行比较，并与手动方法在 3D 空间中检测树突棘的结果进行比较。实验结果和比较表明，所提出的方法能够自动且准确地检测、分割和定量 3D 图像中的 MSNs 树突棘。
