3D Microscopy Images Research Articles

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COMPUTING THE BEST HIGH-RESOLUTION 3D TISSUE IMAGESReal-time, 3D microscopic tissue imaging could be a revolution for medical fields, such as cancer diagnosis, minimallyinvasive surgery and ophthalmology. University of Illinois researchers have developed a technique to computationallycorrect for aberrations in optical tomography, bringing the future of medical imaging into focus.

Strain Rate Dependent Ductile-to-Brittle Transition of Graphite Platelet Reinforced Vinyl Ester Nanocomposites

In previous research, the fractal dimensions of fractured surfaces of vinyl ester based nanocomposites were estimated applying classical method on 3D digital microscopic images. The fracture energy and fracture toughness were obtained from fractal dimensions. A noteworthy observation, the strain rate dependent ductile-to-brittle transition of vinyl ester based nanocomposites, is reinvestigated in the current study. The candidate materials of xGnP (exfoliated graphite nanoplatelets) reinforced and with additional CTBN (Carboxyl Terminated Butadiene Nitrile) toughened vinyl ester based nanocomposites that are subjected to both quasi-static and high strain rate indirect tensile load using the traditional Brazilian test method. High-strain rate indirect tensile testing is performed with a modified Split-Hopkinson Pressure Bar (SHPB). Pristine vinyl ester shows ductile deformation under quasi-static loading and brittle failure when subjected to high-strain rate loading. This observation reconfirms the previous research findings on strain rate dependent ductile-to-brittle transition of this material system. Investigation of both quasi-static and dynamic indirect tensile test responses show the strain rate effect on the tensile strength and energy absorbing capacity of the candidate materials. Contribution of nanoreinforcement to the tensile properties is reported in this paper.

A proof of principle study on the extraction of biochemical and biomechanical properties from the same tumour cells using 3D confocal Raman and atomic force microscopy imaging – Towards a better understanding of tumour progression

A proof of principle study on the extraction of biochemical and biomechanical properties from the same tumour cells using 3D confocal Raman and atomic force microscopy imaging – Towards a better understanding of tumour progression

Active graph matching for automatic joint segmentation and annotation of C. elegans.

In this work we present a novel technique we term active graph matching, which integrates the popular active shape model into a sparse graph matching problem. This way we are able to combine the benefits of a global, statistical deformation model with the benefits of a local deformation model in form of a second-order random field. We present a new iterative energy minimization technique which achieves empirically good results. This enables us to exceed state-of-the art results for the task of annotating nuclei in 3D microscopic images of C. elegans. Furthermore with the help of the generalized Hough transform we are able to jointly segment and annotate a large set of nuclei in a fully automatic fashion for the first time.

A pipeline for neuron reconstruction based on spatial sliding volume filter seeding.

Neuron's shape and dendritic architecture are important for biosignal transduction in neuron networks. And the anatomy architecture reconstruction of neuron cell is one of the foremost challenges and important issues in neuroscience. Accurate reconstruction results can facilitate the subsequent neuron system simulation. With the development of confocal microscopy technology, researchers can scan neurons at submicron resolution for experiments. These make the reconstruction of complex dendritic trees become more feasible; however, it is still a tedious, time consuming, and labor intensity task. For decades, computer aided methods have been playing an important role in this task, but none of the prevalent algorithms can reconstruct full anatomy structure automatically. All of these make it essential for developing new method for reconstruction. This paper proposes a pipeline with a novel seeding method for reconstructing neuron structures from 3D microscopy images stacks. The pipeline is initialized with a set of seeds detected by sliding volume filter (SVF), and then the open curve snake is applied to the detected seeds for reconstructing the full structure of neuron cells. The experimental results demonstrate that the proposed pipeline exhibits excellent performance in terms of accuracy compared with traditional method, which is clearly a benefit for 3D neuron detection and reconstruction.

A Kalman Filter Approach for Denoising and Deblurring 3-D Microscopy Images

This paper proposes a new method for removing noise and blurring from 3D microscopy images. The main contribution is the definition of a space-variant generating model of a 3-D signal, which is capable to stochastically describe a wide class of 3-D images. Unlike other approaches, the space-variant structure allows the model to consider the information on edge locations, if available. A suitable description of the image acquisition process, including blurring and noise, is then associated to the model. A state-space realization is finally derived, which is amenable to the application of standard Kalman filter as an image restoration algorithm. The so obtained method is able to remove, at each spatial step, both blur and noise, via a linear minimum variance recursive one-shot procedure, which does not require the simultaneous processing of the whole image. Numerical results on synthetic and real microscopy images confirm the merit of the approach.

Identifying Diagonal Cutter Marks on Thin Wires Using 3D Imaging

We present work on matching 2-mm-thick wires using optical 3D imaging methods. Marks on such small surfaces are difficult to match using a comparison microscope as this 2D imaging method does not provide height data about the sample surface. Moreover, these 2D microscopy images may be affected by illumination. Hence, the reference and investigated sample should be present at the same time. We employed scanning white light interferometry and confocal microscopy to provide quantitative 3D profiles for reliable comparison of samples that are unavailable for simultaneous analysis. We show that 3D profiling offers a solution by allowing illumination-independent sample comparison. We correctly identified 74 of 80 profiles using consecutive matching striae (CMS) criteria, and we were able to match samples based on profiles measured using different 3D imaging devices. The results suggest that the used methods allow matching cutter marks on thin wires, which has been difficult previously.

Measuring image resolution in optical nanoscopy

Resolution in optical nanoscopy (or super-resolution microscopy) depends on the localization uncertainty and density of single fluorescent labels and on the sample's spatial structure. Currently there is no integral, practical resolution measure that accounts for all factors. We introduce a measure based on Fourier ring correlation (FRC) that can be computed directly from an image. We demonstrate its validity and benefits on two-dimensional (2D) and 3D localization microscopy images of tubulin and actin filaments. Our FRC resolution method makes it possible to compare achieved resolutions in images taken with different nanoscopy methods, to optimize and rank different emitter localization and labeling strategies, to define a stopping criterion for data acquisition, to describe image anisotropy and heterogeneity, and even to estimate the average number of localizations per emitter. Our findings challenge the current focus on obtaining the best localization precision, showing instead how the best image resolution can be achieved as fast as possible.

APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree

Tracing of neuron morphology is an essential technique in computational neuroscience. However, despite a number of existing methods, few open-source techniques are completely or sufficiently automated and at the same time are able to generate robust results for real 3D microscopy images. We developed all-path-pruning 2.0 (APP2) for 3D neuron tracing. The most important idea is to prune an initial reconstruction tree of a neuron's morphology using a long-segment-first hierarchical procedure instead of the original termini-first-search process in APP. To further enhance the robustness of APP2, we compute the distance transform of all image voxels directly for a gray-scale image, without the need to binarize the image before invoking the conventional distance transform. We also design a fast-marching algorithm-based method to compute the initial reconstruction trees without pre-computing a large graph. This method allows us to trace large images. We bench-tested APP2 on ~700 3D microscopic images and found that APP2 can generate more satisfactory results in most cases than several previous methods. The software has been implemented as an open-source Vaa3D plugin. The source code is available in the Vaa3D code repository http://vaa3d.org. Supplementary data are available at Bioinformatics online.

Non-destructive microtomography-based imaging and measuring laboratory-induced degradation of travertine, a random heterogeneous geomaterial used in urban heritage

The aim of this study is to apply X-ray microfocus computed microtomography (μ-XCT), a promising non-destructive 3D microscopy imaging technique, based on measurements of X-ray linear path attenuation coefficient, in order to study a Portuguese travertine, a random heterogeneous geomaterial used in urban heritage constructions. This study evaluates the impact of soluble sulphate salt–induced decay phenomena on texture characteristics at a micrometric scale. This is done to better describing, imaging, measuring and understanding the impact of an artificially induced sulphate-decay process on petrographic/petrophysical properties. A Portuguese travertine was chosen as the object of our study. Its laboratory-induced changes were systematically monitored, using non-destructive techniques, to determine voids (pores + fractures)/matrix fractions and size spectrum evolution based on 3-D images. This investigation demonstrates that the μ-XCT potential constitutes a valid complementary tool when analysing decay processes of complex natural materials in different environmental conditions. It clearly provides suggestive and important qualitative and/or quantitative estimates, at different spatial scales, of environmentally induced stone voids/matrix network spatial structure-texture evolution correlations. Further qualitative/quantitative parameterization assessment and statistical validation will be the next step to be taken on this study.

A flexible and robust approach for segmenting cell nuclei from 2D microscopy images using supervised learning and template matching

We describe a new supervised learning-based template matching approach for segmenting cell nuclei from microscopy images. The method uses examples selected by a user for building a statistical model that captures the texture and shape variations of the nuclear structures from a given dataset to be segmented. Segmentation of subsequent, unlabeled, images is then performed by finding the model instance that best matches (in the normalized cross correlation sense) local neighborhood in the input image. We demonstrate the application of our method to segmenting nuclei from a variety of imaging modalities, and quantitatively compare our results to several other methods. Quantitative results using both simulated and real image data show that, while certain methods may work well for certain imaging modalities, our software is able to obtain high accuracy across several imaging modalities studied. Results also demonstrate that, relative to several existing methods, the template-based method we propose presents increased robustness in the sense of better handling variations in illumination, variations in texture from different imaging modalities, providing more smooth and accurate segmentation borders, as well as handling better cluttered nuclei.

Real-time GPU-based 3D Deconvolution.

Confocal microscopy is an oft-used technique in biology. Deconvolution of 3D images reduces blurring from out-of-focus light and enables quantitative analyses, but existing software for deconvolution is slow and expensive. We present a parallelized software method that runs within ImageJ and deconvolves 3D images ~100 times faster than conventional software (few seconds per image) by running on a low-cost graphics processor board (GPU). We demonstrate the utility of this software by analyzing microclusters of T cell receptors in the immunological synapse of a CD4 + T cell and dendritic cell. This software provides a low-cost and rapid way to improve the accuracy of 3D microscopic images obtained by any method.

Extracting 3D cell parameters from dense tissue environments: application to the development of the mouse heart

In developmental biology, quantitative tools to extract features from fluorescence microscopy images are becoming essential to characterize organ morphogenesis at the cellular level. However, automated image analysis in this context is a challenging task, owing to perturbations induced by the acquisition process, especially in organisms where the tissue is dense and opaque. We propose an automated framework for the segmentation of 3D microscopy images of highly cluttered environments such as developing tissues. The approach is based on a partial differential equation framework that jointly takes advantage of the nuclear and cellular membrane information to enable accurate extraction of nuclei and cells in dense tissues. This framework has been used to study the developing mouse heart, allowing the extraction of quantitative information such as the cell cycle duration; the method also provides qualitative information on cell division and cell polarity through the creation of 3D orientation maps that provide novel insight into tissue organization during organogenesis.

Visualization and analysis of 3D microscopic images.

In a wide range of biological studies, it is highly desirable to visualize and analyze three-dimensional (3D) microscopic images. In this primer, we first introduce several major methods for visualizing typical 3D images and related multi-scale, multi-time-point, multi-color data sets. Then, we discuss three key categories of image analysis tasks, namely segmentation, registration, and annotation. We demonstrate how to pipeline these visualization and analysis modules using examples of profiling the single-cell gene-expression of C. elegans and constructing a map of stereotyped neurite tracts in a fruit fly brain.

Automatic Extraction of Nuclei Centroids of Mouse Embryonic Cells from Fluorescence Microscopy Images

Accurate identification of cell nuclei and their tracking using three dimensional (3D) microscopic images is a demanding task in many biological studies. Manual identification of nuclei centroids from images is an error-prone task, sometimes impossible to accomplish due to low contrast and the presence of noise. Nonetheless, only a few methods are available for 3D bioimaging applications, which sharply contrast with 2D analysis, where many methods already exist. In addition, most methods essentially adopt segmentation for which a reliable solution is still unknown, especially for 3D bio-images having juxtaposed cells. In this work, we propose a new method that can directly extract nuclei centroids from fluorescence microscopy images. This method involves three steps: (i) Pre-processing, (ii) Local enhancement, and (iii) Centroid extraction. The first step includes two variations: first variation (Variant-1) uses the whole 3D pre-processed image, whereas the second one (Variant-2) modifies the preprocessed image to the candidate regions or the candidate hybrid image for further processing. At the second step, a multiscale cube filtering is employed in order to locally enhance the pre-processed image. Centroid extraction in the third step consists of three stages. In Stage-1, we compute a local characteristic ratio at every voxel and extract local maxima regions as candidate centroids using a ratio threshold. Stage-2 processing removes spurious centroids from Stage-1 results by analyzing shapes of intensity profiles from the enhanced image. An iterative procedure based on the nearest neighborhood principle is then proposed to combine if there are fragmented nuclei. Both qualitative and quantitative analyses on a set of 100 images of 3D mouse embryo are performed. Investigations reveal a promising achievement of the technique presented in terms of average sensitivity and precision (i.e., 88.04% and 91.30% for Variant-1; 86.19% and 95.00% for Variant-2), when compared with an existing method (86.06% and 90.11%), originally developed for analyzing C. elegans images.

Improving segmentation of 3D touching cell nuclei using flow tracking on surface meshes

Automatic segmentation of touching cell nuclei in 3D microscopy images is of great importance in bioimage informatics and computational biology. This paper presents a novel method for improving 3D touching cell nuclei segmentation. Given binary touching nuclei by the method in Li et al. (2007), our method herein consists of several steps: surface mesh reconstruction and curvature information estimation; direction field diffusion on surface meshes; flow tracking on surface meshes; and projection of surface mesh segmentation to volumetric images. The method is validated on both synthesised and real 3D touching cell nuclei images, demonstrating its validity and effectiveness.

Soft x-ray tomoholography

We demonstrate an x-ray imaging method that combines Fourier transform holography with tomography (‘tomoholography’) for three-dimensional (3D) microscopic imaging. A 3D image of a diatom shell with a spatial resolution of 140 nm is presented. The experiment is realized by using a small gold sphere as the reference wave source for holographic imaging. This setup allows us to rotate the sample and to collect a number of 2D projections for tomography.

Quantifying size-dependent interactions between fluorescently labeled polystyrene nanoparticles and mammalian cells

BackgroundNanoparticles (NPs) are currently used in a wide variety of fields such as technology, medicine and industry. Due to the novelty of these applications and to ensure their success, a precise characterization of the interactions between NPs and cells is essential.FindingsThe current study explores the uptake of polystyrene NPs by 1321N1 human astrocytoma and A549 human lung carcinoma cell lines. In this work we show for the first time a comparison of the uptake rates of fluorescently labeled carboxylated polystyrene (PS) NPs of different sizes (20, 40 and 100 nm) in two different cell types, keeping the number of NPs per unit volume constant for all sizes. We propose a reliable methodology to control the dose of fluorescently labeled NPs, by counting individual NPs using automated particle detection from 3D confocal microscopy images. The possibility of detecting individual NPs also allowed us to calculate the size of each nanoparticle and compare the fluorescence of single NPs across different sizes, thereby providing a robust platform for normalization of NP internalization experiments as measured by flow cytometry.ConclusionsOur findings show that 40 nm NPs are internalized faster than 20 nm or 100 nm particles in both cell lines studied, suggesting that there is a privileged size gap in which the internalization of NPs is higher.

Cryo-image analysis of tumor cell migration, invasion, and dispersal in a mouse xenograft model of human glioblastoma multiforme.

The goals of this study were to create cryo-imaging methods to quantify characteristics (size, dispersal, and blood vessel density) of mouse orthotopic models of glioblastoma multiforme (GBM) and to enable studies of tumor biology, targeted imaging agents, and theranostic nanoparticles. Green fluorescent protein-labeled, human glioma LN-229 cells were implanted into mouse brain. At 20-38days, cryo-imaging gave whole brain, 4-GB, 3D microscopic images of bright field anatomy, including vasculature, and fluorescent tumor. Image analysis/visualization methods were developed. Vessel visualization and segmentation methods successfully enabled analyses. The main tumor mass volume, the number of dispersed clusters, the number of cells/cluster, and the percent dispersed volume all increase with age of the tumor. Histograms of dispersal distance give a mean and median of 63 and 56μm, respectively, averaged over all brains. Dispersal distance tends to increase with age of the tumors. Dispersal tends to occur along blood vessels. Blood vessel density did not appear to increase in and around the tumor with this cell line. Cryo-imaging and software allow, for the first time, 3D, whole brain, microscopic characterization of a tumor from a particular cell line. LN-229 exhibits considerable dispersal along blood vessels, a characteristic of human tumors that limits treatment success.

High-resolution quantitative phase microscopic imaging in deep UV with phase retrieval

High-resolution three-dimensional (3D) microscopic imaging requires the use of short wavelengths. Quantitative 3D imaging techniques, such as digital holographic microscopy, require interference between the object beam and a known reference background for the extraction of phase information. At shorter wavelengths, due to short coherence lengths, it may be difficult to implement a two-beam off-axis setup. Thus, a single-beam technique, which provides complete phase information, may be better suited for short wavelengths. This Letter describes the development of a quantitative microscopy technique at 193 nm using multiple intensity samplings and phase retrieval.