Literature DB >> 18819835

Robust 3D reconstruction and identification of dendritic spines from optical microscopy imaging.

Firdaus Janoos1, Kishore Mosaliganti, Xiaoyin Xu, Raghu Machiraju, Kun Huang, Stephen T C Wong.   

Abstract

In neurobiology, the 3D reconstruction of neurons followed by the identification of dendritic spines is essential for studying neuronal morphology, function and biophysical properties. Most existing methods suffer from problems of low reliability, poor accuracy and require much user interaction. In this paper, we present a method to reconstruct dendrites using a surface representation of the neuron. The skeleton of the dendrite is extracted by a procedure based on the medial geodesic function that is robust and topology preserving, and it is used to accurately identify spines. The sensitivity of the algorithm on the various parameters is explored in detail and the method is shown to be robust.

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Year:  2008        PMID: 18819835      PMCID: PMC2663851          DOI: 10.1016/j.media.2008.06.019

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  28 in total

1.  An image analysis algorithm for dendritic spines.

Authors:  Ingrid Y Y Koh; W Brent Lindquist; Karen Zito; Esther A Nimchinsky; Karel Svoboda
Journal:  Neural Comput       Date:  2002-06       Impact factor: 2.026

Review 2.  Structure-stability-function relationships of dendritic spines.

Authors:  Haruo Kasai; Masanori Matsuzaki; Jun Noguchi; Nobuaki Yasumatsu; Hiroyuki Nakahara
Journal:  Trends Neurosci       Date:  2003-07       Impact factor: 13.837

3.  Digital overlay of fluorescein angiograms and fundus images for treatment of subretinal neovascularization.

Authors:  T M Clark; W R Freeman; M H Goldbaum
Journal:  Retina       Date:  1992       Impact factor: 4.256

4.  Dendritic and somatic appendages of identified rubrospinal neurons of the cat.

Authors:  C J Wilson; F Murakami; H Katsumaru; N Tsukahara
Journal:  Neuroscience       Date:  1987-07       Impact factor: 3.590

5.  New methods for the computer-assisted 3-D reconstruction of neurons from confocal image stacks.

Authors:  Stephan Schmitt; Jan Felix Evers; Carsten Duch; Michael Scholz; Klaus Obermayer
Journal:  Neuroimage       Date:  2004-12       Impact factor: 6.556

6.  Detection of blood vessels in retinal images using two-dimensional matched filters.

Authors:  S Chaudhuri; S Chatterjee; N Katz; M Nelson; M Goldbaum
Journal:  IEEE Trans Med Imaging       Date:  1989       Impact factor: 10.048

7.  Dendritic spines are susceptible to structural alterations induced by degeneration of their presynaptic afferents.

Authors:  G Benshalom; E L White
Journal:  Brain Res       Date:  1988-03-08       Impact factor: 3.252

8.  Abnormal dendritic spines in fragile X knockout mice: maturation and pruning deficits.

Authors:  T A Comery; J B Harris; P J Willems; B A Oostra; S A Irwin; I J Weiler; W T Greenough
Journal:  Proc Natl Acad Sci U S A       Date:  1997-05-13       Impact factor: 11.205

9.  Automated tracing and volume measurements of neurons from 3-D confocal fluorescence microscopy data.

Authors:  A R Cohen; B Roysam; J N Turner
Journal:  J Microsc       Date:  1994-02       Impact factor: 1.758

10.  Quantification of dendritic spine populations using image analysis and a tilting disector.

Authors:  D A Rusakov; M G Stewart
Journal:  J Neurosci Methods       Date:  1995-08       Impact factor: 2.390

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  12 in total

1.  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

2.  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

3.  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

Review 4.  Methods of dendritic spine detection: from Golgi to high-resolution optical imaging.

Authors:  J J Mancuso; Y Chen; X Li; Z Xue; S T C Wong
Journal:  Neuroscience       Date:  2012-04-20       Impact factor: 3.590

5.  Automated condition-invariable neurite segmentation and synapse classification using textural analysis-based machine-learning algorithms.

Authors:  Umasankar Kandaswamy; Ziv Rotman; Dana Watt; Ian Schillebeeckx; Valeria Cavalli; Vitaly A Klyachko
Journal:  J Neurosci Methods       Date:  2012-12-20       Impact factor: 2.390

6.  A NOVEL SURFACE-BASED GEOMETRIC APPROACH FOR 3D DENDRITIC SPINE DETECTION FROM MULTI-PHOTON EXCITATION MICROSCOPY IMAGES.

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

7.  A neurocomputational method for fully automated 3D dendritic spine detection and segmentation of medium-sized spiny neurons.

Authors:  Yong Zhang; Kun Chen; Matthew Baron; Merilee A Teylan; Yong Kim; Zhihuan Song; Paul Greengard; Stephen T C Wong
Journal:  Neuroimage       Date:  2010-01-25       Impact factor: 6.556

8.  Ultrastructural analysis of dendritic spine necks reveals a continuum of spine morphologies.

Authors:  Netanel Ofer; Daniel R Berger; Narayanan Kasthuri; Jeff W Lichtman; Rafael Yuste
Journal:  Dev Neurobiol       Date:  2021-05-30       Impact factor: 3.964

9.  Automated 4D analysis of dendritic spine morphology: applications to stimulus-induced spine remodeling and pharmacological rescue in a disease model.

Authors:  Sharon A Swanger; Xiaodi Yao; Christina Gross; Gary J Bassell
Journal:  Mol Brain       Date:  2011-10-07       Impact factor: 4.041

10.  Detection of Dendritic Spines Using Wavelet-Based Conditional Symmetric Analysis and Regularized Morphological Shared-Weight Neural Networks.

Authors:  Shuihua Wang; Mengmeng Chen; Yang Li; Yudong Zhang; Liangxiu Han; Jane Wu; Sidan Du
Journal:  Comput Math Methods Med       Date:  2015-11-24       Impact factor: 2.238

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