Visualizing in vivo brain neural structures using volume rendered feature spaces (original) (raw)
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BMC bioinformatics, 2014
BackgroundRecent advances in microscopy enable the acquisition of large numbers of tomographic images from living tissues. Three-dimensional microscope images are often displayed with volume rendering by adjusting the transfer functions. However, because the emissions from fluorescent materials and the optical properties based on point spread functions affect the imaging results, the intensity value can differ locally, even in the same structure. Further, images obtained from brain tissues contain a variety of neural structures such as dendrites and axons with complex crossings and overlapping linear structures. In these cases, the transfer functions previously used fail to optimize image generation, making it difficult to explore the connectivity of these tissues.ResultsThis paper proposes an interactive visual exploration method by which the transfer functions are modified locally and interactively based on multidimensional features in the images. A direct editing interface is als...
Automated 3-D Detection of Dendritic Spines from In Vivo Two-Photon Image Stacks
Neuroinformatics, 2017
Despite the significant advances in the development of automated image analysis algorithms for the detection and extraction of neuronal structures, current software tools still have numerous limitations when it comes to the detection and analysis of dendritic spines. The problem is especially challenging in in vivo imaging, where the difficulty of extracting morphometric properties of spines is compounded by lower image resolution and contrast levels native to two-photon laser microscopy. To address this challenge, we introduce a new computational framework for the automated detection and quantitative analysis of dendritic spines in vivo multi-photon imaging. This framework includes: (i) a novel preprocessing algorithm enhancing spines in a way that they are included in the binarized volume produced during the segmentation of foreground from background; (ii) the mathematical foundation of this algorithm, and (iii) an algorithm for the detection of spine locations in reference to centerline trace and separating them from
Neuroscience, 2005
Cognitive impairment in normal aging and neurodegenerative diseases is accompanied by altered morphologies on multiple scales. Understanding of the role of these structural changes in producing functional deficits in brain aging and neuropsychiatric disorders requires accurate three-dimensional representations of neuronal morphology, and realistic biophysical modeling that can directly relate structural changes to altered neuronal firing patterns. To date however, tools capable of resolving, digitizing and analyzing neuronal morphology on both local and global scales, and with sufficient throughput and automation, have been lacking. The precision of existing image analysis-based morphometric tools is restricted at the finest scales, where resolution of fine dendritic features and spine geometry is limited by the skeletonization methods used, and by quantization errors arising from insufficient imaging resolution. We are developing techniques for imaging, reconstruction and analysis of neuronal morphology that capture both local and global structural variation. To minimize quantization error and evaluate more precisely the fine geometry of dendrites and spines, we introduce a new shape analysis technique, the Rayburst sampling algorithm that uses the original grayscale data rather than the segmented images for precise, continuous radius estimation, and multidirectional radius sampling to represent non-circular branch cross-sections and anisotropic structures such as dendritic spine heads, with greater accuracy. We apply the Rayburst technique to 3D neuronal shape analysis at different scales. We reconstruct and digitize entire neurons from stacks of laser-scanning microscopy images, as well as globally complex structures such as multineuron networks and microvascular networks. We also introduce imaging techniques necessary to recover detailed information on three-dimensional mass distribution and surface roughness of amyloid beta plaques from human Alzheimer's disease patients and from the Tg2576 mouse that expresses the "Swedish" mutation of the amyloid precursor protein.
Biomedical Optics Express, 2021
Although neuronal density analysis on human brain slices is available from stereological studies, data on the spatial distribution of neurons in 3D are still missing. Since the neuronal organization is very inhomogeneous in the cerebral cortex, it is critical to map all neurons in a given volume rather than relying on sparse sampling methods. To achieve this goal, we implement a new tissue transformation protocol to clear and label human brain tissues and we exploit the high-resolution optical sectioning of two-photon fluorescence microscopy to perform 3D mesoscopic reconstruction. We perform neuronal mapping of 100mm3 human brain samples and evaluate the volume and density distribution of neurons from various areas of the cortex originating from different subjects (young, adult, and elderly, both healthy and pathological). The quantitative evaluation of the density in combination with the mean volume of the thousands of neurons identified within the specimens, allow us to determine...
Neuroanatomical Tract-Tracing 3, 2006
Recently, advances in optical imaging of the living brain slice preparation have permitted neuronal circuitry to be examined at multiple levels, ranging from individual synaptic contacts on dendrites to whole populations of neurons in a network. In this chapter, we describe three techniques that, together, enable a powerful dissection of neuronal circuits across multiple space scales. We describe methods for (1) combining whole-cell recording with two-photon calcium imaging and electron microscopic reconstruction to examine the functions of individual synapses and dendrites during synaptic stimulation, (2) imaging hundreds of neurons in the brain slice simultaneously to examine the spatiotemporal dynamics of activity in living neuronal networks, and (3) performing an unbiased, quantitative analysis of neuronal morphology that is increasingly necessary in light of the multiparametric structural diversity of distinct neuronal subclasses.
Automatic Graph-Based Modeling of Brain Microvessels Captured With Two-Photon Microscopy
IEEE Journal of Biomedical and Health Informatics, 2018
Graph models of cerebral vasculature derived from 2-photon microscopy have shown to be relevant to study brain microphysiology. Automatic graphing of these microvessels remain problematic due to the vascular network complexity and 2-photon sensitivity limitations with depth. In this work, we propose a fully automatic processing pipeline to address this issue. The modeling scheme consists of a fully-convolution neural network to segment microvessels, a 3D surface model generator and a geometry contraction algorithm to produce graphical models with a single connected component. Quantitative assessment using NetMets metrics, at a tolerance of 60 μm, false negative and false positive geometric error rates are 3.8% and 4.2%, respectively, whereas false negative and false positive topological error rates are 6.1% and 4.5%, respectively. Our qualitative evaluation confirms the efficiency of our scheme in generating useful and accurate graphical models.
Methods (San Diego, Calif.), 2003
Experimental and theoretical studies demonstrate that both global dendritic branching topology and fine spine geometry are crucial determinants of neuronal function, its plasticity and pathology. Importantly, simulation studies indicate that the interaction between local and global morphologic properties is pivotal in determining dendritic information processing and the induction of synapse-specific plasticity. The ability to reconstruct and quantify dendritic processes at high resolution is therefore an essential prerequisite to understanding the structural determinants of neuronal function. Existing methods of digitizing 3D neuronal structure use interactive manual computer tracing from 2D microscopy images. This method is time-consuming, subjective and lacks precision. In particular, fine details of dendritic varicosities, continuous dendritic taper, and spine morphology cannot be captured by these systems. We describe a technique for automated reconstruction of 3D neuronal morphology from multiple stacks of tiled confocal and multiphoton laser scanning microscopy (CLSM and MPLSM) images. The system is capable of representing both global and local structural variations, including gross dendritic branching topology, dendritic varicosities, and fine spine morphology with sufficient resolution for accurate 3D morphometric analyses and realistic biophysical compartment modeling. Our system provides a much needed tool for automated digitization and reconstruction of 3D neuronal morphology that reliably captures detail on spatial scales spanning several orders of magnitude, that avoids the subjective errors that arise during manual tracing with existing digitization systems, and that runs on a standard desktop workstation.
Automatic reconstruction of dendrite morphology from optical section stacks
2006
The function of the human brain arises from computations that occur within and among billions of nerve cells known as neurons. A neuron is composed primarily of a cell body (soma) from which emanates a collection of branching structures (dendrites). How neuronal signals are processed is dependent on the dendrites' specific morphology and distribution of voltage-gated ion channels. To understand this processing, it is necessary to acquire an accurate structural analysis of the cell. Toward this end, we present an automated reconstruction system which extracts the morphology of neurons imaged from confocal and multiphoton microscopes. As we place emphasis on this being a rapid (and therefore automated) process, we have developed several techniques that provide high-quality reconstructions with minimal human interaction. In addition to generating a tree of connected cylinders representing the reconstructed neuron, a computational model is also created for purposes of performing functional simulations. We present visual and statistical results from reconstructions performed both on real image volumes and on noised synthetic data from the Duke-Southampton archive.
Non-Photorealistic Rendering of Neural Cells from their Morphological Description
J. Univers. Comput. Sci., 2015
Gaining a better understanding of the human brain continues to be one of the great- est and most elusive of challenges. Its extreme complexity can only be addressed through the coordinated and collaborative work of researchers from a range of disciplines. 3D visualization has proven to be a useful tool for simplifying the analysis of complex systems, where gaining meaningful understanding from unstructured raw data is almost impossible, such as in the case of the brain. This paper presents a novel approach for visualizing neurons directly from the mor- phological descriptions extracted by neuroscience laboratories, pursuing two goals: improving the readability of complex neuronal scenarios and avoiding the need to store 3D models of the intricate geometry of neurons, since such models are demanding of computer resources. The proposed rendering method involves illustration techniques that facilitate the visual analysis of dense neural scenes. The work presented here brings the field ...
DTI volume rendering techniques for visualising the brain anatomy
International Congress Series, 2005
Over the past few years Diffusion Tensor Imaging (DTI) has become an increasingly popular method for imaging the brain anatomy and diagnosing a variety of neurodegenerative diseases. Unfortunately the size and multi-dimensional nature of diffusion tensor data sets makes it difficult to understand them. We use illuminated streamlines to compute high quality dense 3D visualisations of the 3D nerve fibre structure. Nerve fibres are extracted using a numerical integration technique and a fuzzy classifier which represents the probability that a sample point represents grey matter, white matter or Cerebral Spinal Fluid (CSF). We present two novel methods which improve the perception of the 3D arrangements of fibre tracts. The first method is a hardware accelerated algorithm which represents fibres as semi-transparent tubes with emphasised silhouettes. Because of the semi-transparent nature of the tubes inside structures are revealed. The enhancement of tube silhouettes improves the identification of individual fibre tracts and their 3D arrangement. The second method uses direct volume rendering and multiple colour and transparency look-up tables to represent the directional information of the nerve fibre structure and other tissue types simultaneously. The method can be used to represent finer details depending on the resolution of the noise texture employed. Depending on the choice of the opacity transfer functions fibre tracts can be represented semi-transparent or nearly opaque.