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Population-scale three-dimensional reconstruction and quantitative profiling of microglia arbors.
Bioinformatics
July 2015
Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA, Center for Integrative Brain Research, Seattle Children's Hospital, Seattle, WA 98101, USA and Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
Motivation: The arbor morphologies of brain microglia are important indicators of cell activation. This article fills the need for accurate, robust, adaptive and scalable methods for reconstructing 3-D microglial arbors and quantitatively mapping microglia activation states over extended brain tissue regions.
Results: Thick rat brain sections (100-300 µm) were multiplex immunolabeled for IBA1 and Hoechst, and imaged by step-and-image confocal microscopy with automated 3-D image mosaicing, producing seamless images of extended brain regions (e.
Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python.
Front Neuroinform
May 2014
BioImage Analytics Laboratory, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA.
In this article, we describe the use of Python for large-scale automated server-based bio-image analysis in FARSIGHT, a free and open-source toolkit of image analysis methods for quantitative studies of complex and dynamic tissue microenvironments imaged by modern optical microscopes, including confocal, multi-spectral, multi-photon, and time-lapse systems. The core FARSIGHT modules for image segmentation, feature extraction, tracking, and machine learning are written in C++, leveraging widely used libraries including ITK, VTK, Boost, and Qt. For solving complex image analysis tasks, these modules must be combined into scripts using Python.
View Article and Find Full Text PDFThe FARSIGHT trace editor: an open source tool for 3-D inspection and efficient pattern analysis aided editing of automated neuronal reconstructions.
Neuroinformatics
September 2011
Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204, USA.
Associative image analysis: a method for automated quantification of 3D multi-parameter images of brain tissue.
J Neurosci Methods
May 2008
Center for Neural Communication Technology, New York State Department of Health, Wadsworth Center, Albany, NY 12201-0509, USA.
Brain structural complexity has confounded prior efforts to extract quantitative image-based measurements. We present a systematic 'divide and conquer' methodology for analyzing three-dimensional (3D) multi-parameter images of brain tissue to delineate and classify key structures, and compute quantitative associations among them. To demonstrate the method, thick ( approximately 100 microm) slices of rat brain tissue were labeled using three to five fluorescent signals, and imaged using spectral confocal microscopy and unmixing algorithms.
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