Literature DB >> 17654649

Automatic dendritic spine analysis in two-photon laser scanning microscopy images.

Wenjia Bai1, Xiaobo Zhou, Liang Ji, Jie Cheng, Stephen T C Wong.   

Abstract

Dendritic spine expression plays an important role in the central nervous system. Modern fluorescence microscopy and green fluorescent protein technology have facilitated the research on spines. To quantitatively analyze the spines in fluorescence microscopy images, an automatic dendritic spine analysis method is proposed. Because of the limit of axial resolution, our method is designed to process the projection image along the z-axis and analyze the lateral spines. The method can automatically extract the dendrite centerlines and segment the spines along the dendrites according to width-based criteria. The criteria utilize a common morphological feature of the spines. It can detect some shapes of spines missed by previous methods. In addition, the proposed method is automatic once a few parameters are set. Spine numbers, lengths, and densities, which biologists are interested in, are analyzed both manually and automatically. The results of the two methods match well. The proposed method provides automatic and accurate dendritic spine analysis. It can serve as a useful tool for spine image analysis to avoid tedious manual labor.

Entities:  

Mesh:

Year:  2007        PMID: 17654649     DOI: 10.1002/cyto.a.20431

Source DB:  PubMed          Journal:  Cytometry A        ISSN: 1552-4922            Impact factor:   4.355


  7 in total

1.  Cell segmentation using front vector flow guided active contours.

Authors:  Fuhai Li; Xiaobo Zhou; Hong Zhao; Stephen T C Wong
Journal:  Med Image Comput Comput Assist Interv       Date:  2009

2.  Oriented Markov random field based dendritic spine segmentation for fluorescence microscopy images.

Authors:  Jie Cheng; Xiaobo Zhou; Eric L Miller; Veronica A Alvarez; Bernardo L Sabatini; Stephen T C Wong
Journal:  Neuroinformatics       Date:  2010-10

3.  Automated 3-D Detection of Dendritic Spines from In Vivo Two-Photon Image Stacks.

Authors:  P K Singh; P Hernandez-Herrera; D Labate; M Papadakis
Journal:  Neuroinformatics       Date:  2017-10

4.  Automated three-dimensional reconstruction and morphological analysis of dendritic spines based on semi-supervised learning.

Authors:  Peng Shi; Yue Huang; Jinsheng Hong
Journal:  Biomed Opt Express       Date:  2014-04-17       Impact factor: 3.732

5.  Automated dendritic spine detection using convolutional neural networks on maximum intensity projected microscopic volumes.

Authors:  Xuerong Xiao; Maja Djurisic; Assaf Hoogi; Richard W Sapp; Carla J Shatz; Daniel L Rubin
Journal:  J Neurosci Methods       Date:  2018-08-18       Impact factor: 2.390

6.  ONLINE THREE-DIMENSIONAL DENDRITIC SPINES MOPHOLOGICAL CLASSIFICATION BASED ON SEMI-SUPERVISED LEARNING.

Authors:  Peng Shi; Xiaobo Zhou; Qing Li; Matthew Baron; Merilee A Teylan; Yong Kim; Stephen T C Wong
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2009-06-28

7.  MDL constrained 3-D grayscale skeletonization algorithm for automated extraction of dendrites and spines from fluorescence confocal images.

Authors:  Xiaosong Yuan; Joshua T Trachtenberg; Steve M Potter; Badrinath Roysam
Journal:  Neuroinformatics       Date:  2009-12-11
  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.