A robust co-localisation measurement utilising z-stack image intensity similarities for biological studies

Background

Co-localisation is a widely used measurement in immunohistochemical analysis to determine if fluorescently labelled biological entities, such as cells, proteins or molecules share a same location. However the measurement of co-localisation is challenging due to the complex nature of such fluorescent images, especially when multiple focal planes are captured. The current state-of-art co-localisation measurements of 3-dimensional (3D) image stacks are biased by noise and cross-overs from non-consecutive planes.

Method

In this study, we have developed Co-localisation Intensity Coefficients (CICs) and Co-localisation Binary Coefficients (CBCs), which uses rich z-stack data from neighbouring focal planes to identify similarities between image intensities of two and potentially more fluorescently-labelled biological entities. This was developed using z-stack images from murine organotypic slice cultures from central nervous system tissue, and two sets of pseudo-data. A large amount of non-specific cross-over situations are excluded using this method. This proposed method is also proven to be robust in recognising co-localisations even when images are polluted with a range of noises.

Results

The proposed CBCs and CICs produce robust co-localisation measurements which are easy to interpret, resilient to noise and capable of removing a large amount of false positivity, such as non-specific cross-overs. Performance of this method of measurement is significantly more accurate than existing measurements, as determined statistically using pseudo datasets of known values. This method provides an important and reliable tool for fluorescent 3D neurobiological studies, and will benefit other biological studies which measure fluorescence co-localisation in 3D.     

Enhanced contrast acoustic‐resolution photoacoustic microscopy using double‐stage delay‐multiply‐and‐sum beamformer for vasculature imaging

In acoustic‐resolution photoacoustic microscopy (AR‐PAM) systems, the lateral resolution in the focal zone of the ultrasound (US) transducer is determined by the numerical aperture (NA) of the transducer. To have a high lateral resolution, a large NA is used. However, the larger the NA, the smaller the depth of focus [DOF]. As a result, the lateral resolution is deteriorated at depths out of the focal region. The synthetic aperture focusing technique (SAFT) along with a beamformer can be used to improve the resolution outside the focal region. In this work, for image formation in AR‐PAM, we propose the double‐stage delay‐multiply‐and‐sum (DS_DMAS) algorithm to be combined with SAFT. The proposed method is evaluated experimentally using hair targets and in vivo vasculature imaging. It is shown that DS_DMAS provides a higher resolution and contrast compared to other methods. For the B‐mode images obtained using the hair phantom, the proposed method reduces the average noise level for all the depths by about 134%, 57% and 23%, compared to the original low‐ resolution, SAFT+DAS and SAFT+DMAS methods, respectively. All the results indicate that the proposed method can be an appropriate algorithm for image formation in AR‐PAM systems.      

Deconvolution of images from 3D printed cells in layers on a chip

Layer‐by‐layer cell printing is useful in mimicking layered tissue structures inside the human body and has great potential for being a promising tool in the field of tissue engineering, regenerative medicine, and drug discovery. However, imaging human cells cultured in multiple hydrogel layers in 3D‐printed tissue constructs is challenging as the cells are not in a single focal plane. Although confocal microscopy could be a potential solution for this issue, it compromises the throughput which is a key factor in rapidly screening drug efficacy and toxicity in pharmaceutical industries. With epifluorescence microscopy, the throughput can be maintained at a cost of blurred cell images from printed tissue constructs. To rapidly acquire in‐focus cell images from bioprinted tissues using an epifluorescence microscope, we created two layers of Hep3B human hepatoma cells by printing green and red fluorescently labeled Hep3B cells encapsulated in two alginate layers in a microwell chip. In‐focus fluorescent cell images were obtained in high throughput using an automated epifluorescence microscopy coupled with image analysis algorithms, including three deconvolution methods in combination with three kernel estimation methods, generating a total of nine deconvolution paths. As a result, a combination of Inter‐Level Intra‐Level Deconvolution (ILILD) algorithm and Richardson‐Lucy (RL) kernel estimation proved to be highly useful in bringing out‐of‐focus cell images into focus, thus rapidly yielding more sensitive and accurate fluorescence reading from the cells in different layers. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 34:445–454, 2018     

Confocal microscopy: a tool to visualise differentiation of stem cells into cardiomyocytes

Confocal microscopy offers important advantages compared to conventional epifluorescence microscopy. It works as an "optical microtome" leading to a accurate image resolution of a defined focal plane. Furthermore, the addition of a Nipkow disk on the confocal microscope greatly accelerates the image acquisition, up to 30 frames per second. Nevertheless, the software-assisted mathematical restoration of images acquired using a wide-field microscope allows to get images with a resolution similar to the one obtained in confocal microscopy. These imaging technologies allowed us to monitor on line cardiac differentiation of murine embryonic stem (ES) cells within 3D structures called embryoid bodies. The high rate acquisition of images using the confocal microscope equipped with a Nipkow disk allows to monitor calcium spiking in differentiating cardiomyocytes within embryoid bodies.     

Quadrature demodulation in line‐scan focal modulation microscopy for imaging three‐dimensional zebrafish neural structure

Visualizing biological processes in neuroscience requires in vivo functional imaging at single‐neuron resolution, high image acquisition speed and strong optical sectioning ability. However, due to light scattering of in tissue, very often conventional wide‐field fluorescence microscopes are unable to resolve cells in the presence of a strong out‐of‐focus background. Line‐scan focal modulation microscopy enables high temporal resolution and good optical sectioning ability at the same time. Here we demonstrate a quadrature demodulation method to extract the focal information with an extended frequency bandwidth and therefore higher spatial resolution. The performance of the demodulation scheme in line‐scan focal modulation microscope has been evaluated by performing imaging experiments with fluorescence beads and zebrafish neural structure. Reduced background, reduced artifacts and more detailed morphological information are evident in the obtained images.      

Extended depth-of-focus imaging of chlorophyll fluorescence from intact leaves

Imaging dynamic changes in chlorophyll a fluorescence provides a valuable means with which to examine localised changes in photosynthetic function. Microscope-based systems provide excellent spatial resolution which allows the response of individual cells to be measured. However, such systems have a restricted depth of focus and, as leaves are inherently uneven, only a small proportion of each image at any given focal plane is in focus. In this report we describe the development of algorithms, specifically adapted for imaging chlorophyll fluorescence and photosynthetic function in living plant cells, which allow extended-focus images to be reconstructed from images taken in different focal planes. We describe how these procedures can be used to reconstruct images of chlorophyll fluorescence and calculated photosynthetic parameters, as well as producing a map of leaf topology. The robustness of this procedure is demonstrated using leaves from a number of different plant species. This revised version was published online in June 2006 with corrections to the Cover Date.     

Dual light‐emitting diode‐based multichannel microscopy for whole‐slide multiplane,multispectral and phase imaging

We report the development of a multichannel microscopy for whole‐slide multiplane, multispectral and phase imaging. We use trinocular heads to split the beam path into 6 independent channels and employ a camera array for parallel data acquisition, achieving a maximum data throughput of approximately 1 gigapixel per second. To perform single‐frame rapid autofocusing, we place 2 near‐infrared light‐emitting diodes (LEDs) at the back focal plane of the condenser lens to illuminate the sample from 2 different incident angles. A hot mirror is used to direct the near‐infrared light to an autofocusing camera. For multiplane whole‐slide imaging (WSI), we acquire 6 different focal planes of a thick specimen simultaneously. For multispectral WSI, we relay the 6 independent image planes to the same focal position and simultaneously acquire information at 6 spectral bands. For whole‐slide phase imaging, we acquire images at 3 focal positions simultaneously and use the transport‐of‐intensity equation to recover the phase information. We also provide an open‐source design to further increase the number of channels from 6 to 15. The reported platform provides a simple solution for multiplexed fluorescence imaging and multimodal WSI. Acquiring an instant focal stack without z‐scanning may also enable fast 3‐dimensional dynamic tracking of various biological samples.      

High Fidelity System Modeling for High Quality Image Reconstruction in Clinical CT

Today, while many researchers focus on the improvement of the regularization term in IR algorithms, they pay less concern to the improvement of the fidelity term. In this paper, we hypothesize that improving the fidelity term will further improve IR image quality in low-dose scanning, which typically causes more noise. The purpose of this paper is to systematically test and examine the role of high-fidelity system models using raw data in the performance of iterative image reconstruction approach minimizing energy functional. We first isolated the fidelity term and analyzed the importance of using focal spot area modeling, flying focal spot location modeling, and active detector area modeling as opposed to just flying focal spot motion. We then compared images using different permutations of all three factors. Next, we tested the ability of the fidelity terms to retain signals upon application of the regularization term with all three factors. We then compared the differences between images generated by the proposed method and Filtered-Back-Projection. Lastly, we compared images of low-dose in vivo data using Filtered-Back-Projection, Iterative Reconstruction in Image Space, and the proposed method using raw data. The initial comparison of difference maps of images constructed showed that the focal spot area model and the active detector area model also have significant impacts on the quality of images produced. Upon application of the regularization term, images generated using all three factors were able to substantially decrease model mismatch error, artifacts, and noise. When the images generated by the proposed method were tested, conspicuity greatly increased, noise standard deviation decreased by 90% in homogeneous regions, and resolution also greatly improved. In conclusion, the improvement of the fidelity term to model clinical scanners is essential to generating higher quality images in low-dose imaging.     

High-definition mapping of neural activity using voltage-sensitive dyes

The distribution of patterns of activity in different brain structures has been related to the encoding and processing of sensory information. Consequently, it is important to be able to image the distribution of these patterns to understand basic brain functions. The spatial resolution of voltage-sensitive dye (VSD) methods has recently been enhanced considerably by the use of video imaging techniques. The main factor that now hampers the resolution of VSD patterns is the inherent limitation of the optical systems. Unfortunately, the intrinsic characteristics of VSD images impose important limitations that restrict the use of general deconvolution techniques. To overcomes this problem, in this study an image restoration procedure has been implemented that takes into consideration the limiting characteristics of VSD signals. This technique is based on applying a set of imaging processing steps. First, the signal-to-noise (S/N) ratio of the images was improved to avoid an increase in the noise levels during the deconvolution procedures. For this purpose, a new filter technique was implemented that yielded better results than other methods currently used in optical imaging. Second, focal plane images were deconvolved using a modification of the well-known nearest-neighbor deconvolution algorithm. But to reduce the light exposure of the preparation and simplify image acquisition procedures, adjacent image planes were modeled according to the in-focus image planes and the empirical point spread function (PSF) profiles. Third, resulting focal plane responses were processed to reduce the contribution of optical responses that originate in distant image planes. This method was found to be satisfactory under simulated and real experimental conditions. By comparing the restored and unprocessed images, it was clearly demonstrated that this method can effectively remove the out-of-focus artifacts and produce focal plane images of better quality. Evaluations of the tissue optical properties allowed assessment of the maximum practical optical section thickness using this deconvolution technique in the optical system tested. Determination of the three-dimensional PSF permitted the correct application of deconvolution algorithms and the removal of the contaminating light arising from adjacent as well as distant optical planes. The implementation of this deconvolution approach in salamander olfactory bulb allowed the detailed study of the laminar distribution of voltage-sensitive changes across the bulb layer. It is concluded that (1) this deconvolution procedure is well suited to deconvolved low-contrast images and offers important advantages over other alternatives; (2) this method can be properly used only when the tissue optical properties are first determined; (3) high levels of light scattering in the tissue reduce the optical section capabilities of this technique as well as other deconvolution procedures; and (4) use of the highest numerical aperture in the objectives is advisable because this improves not only the light-collecting efficiency to detect poor-contrast images, but also the spatial frequency differences between adjacent image planes. Under this condition it is possible to overcome some of the limitations imposed by the light scattering/birefringence of the tissue.     

Focal plane array infrared imaging: a new way to analyse leaf tissue

* Here, a new approach to macromolecular imaging of leaf tissue using a multichannel focal plane array (FPA) infrared detector was compared with the proven method of infrared mapping with a synchrotron source, using transverse sections of leaves from a species of Eucalyptus. * A new histological method was developed, ideally suited to infrared spectroscopic analysis of leaf tissue. Spatial resolution and the signal-to-noise ratio of the FPA imaging and synchrotron mapping methods were compared. * An area of tissue 350 microm(2) required approx. 8 h to map using the synchrotron technique and approx. 2 min to image using the FPA. The two methods produced similar infrared images, which differentiated all tissue types in the leaves according to their macromolecular chemistry. * The synchrotron and FPA methods produced similar results, with the synchrotron method having superior signal-to-noise ratio and potentially better spatial resolution, whereas the FPA method had the advantage in terms of data acquisition time, expense and ease of use. FPA imaging offers a convenient, laboratory-based approach to microscopic chemical imaging of leaves.     

Reducing data acquisition for light‐sheet microscopy by extrapolation between imaged planes

Light‐sheet fluorescence microscopy (LSFM) is a powerful technique that can provide high‐resolution images of biological samples. Therefore, this technique offers significant improvement for three‐dimensional (3D) imaging of living cells. However, producing high‐resolution 3D images of a single cell or biological tissues, normally requires high acquisition rate of focal planes, which means a large amount of sample sections. Consequently, it consumes a vast amount of processing time and memory, especially when studying real‐time processes inside living cells. We describe an approach to minimize data acquisition by interpolation between planes using a phase retrieval algorithm. We demonstrate this approach on LSFM data sets and show reconstruction of intermediate sections of the sparse samples. Since this method diminishes the required amount of acquisition focal planes, it also reduces acquisition time of samples as well. Our suggested method has proven to reconstruct unacquired intermediate planes from diluted data sets up to 10× fold. The reconstructed planes were found correlated to the original preacquired samples (control group) with correlation coefficient of up to 90%. Given the findings, this procedure appears to be a powerful method for inquiring and analyzing biological samples.     

Extended-Depth 3D Super-Resolution Imaging Using Probe-Refresh STORM

Single-molecule localization microscopy methods for super-resolution fluorescence microscopy such as STORM (stochastic optical reconstruction microscopy) are generally limited to thin three-dimensional (3D) sections (≤600 nm) because of photobleaching of molecules outside the focal plane. Although multiple focal planes may be imaged before photobleaching by focusing progressively deeper within the sample, image quality is compromised in this approach because the total number of measurable localizations is divided between detection planes. Here, we solve this problem on fixed samples by developing an imaging method that we call probe-refresh STORM (prSTORM), which allows bleached fluorophores to be straightforwardly replaced with nonbleached fluorophores. We accomplish this by immunostaining the sample with DNA-conjugated antibodies and then reading out their distribution using fluorescently-labeled DNA-reporter oligonucleotides that can be fully replaced in successive rounds of imaging. We demonstrate that prSTORM can acquire 3D images over extended depths without sacrificing the density of localizations at any given plane. We also show that prSTORM can be adapted to obtain high-quality, 3D multichannel images with extended depth that would be challenging or impossible to achieve using established probe methods.     

Assessing potential errors of MRI-based measurements of pulmonary blood flow using a detailed network flow model

MRI images of pulmonary blood flow using arterial spin labeling (ASL) measure the delivery of magnetically tagged blood to an image plane during one systolic ejection period. However, the method potentially suffers from two problems, each of which may depend on the imaging plane location: 1) the inversion plane is thicker than the imaging plane, resulting in a gap that blood must cross to be detected in the image; and 2) ASL includes signal contributions from tagged blood in conduit vessels (arterial and venous). By using an in silico model of the pulmonary circulation we found the gap reduced the ASL signal to 64-74% of that in the absence of a gap in the sagittal plane and 53-84% in the coronal. The contribution of the conduit vessels varied markedly as a function of image plane ranging from ～90% of the overall signal in image planes that encompass the central hilar vessels to <20% in peripheral image planes. A threshold cutoff removing voxels with intensities >35% of maximum reduced the conduit vessel contribution to the total ASL signal to ～20% on average; however, planes with large contributions from conduit vessels underestimate acinar flow due to a high proportion of in-plane flow, making ASL measurements of perfusion impractical. In other image planes, perfusion dominated the resulting ASL images with good agreement between ASL and acinar flow. Similarly, heterogeneity of the ASL signal as measured by relative dispersion is a reliable measure of heterogeneity of the acinar flow distribution in the same image planes.     

LeukocyteMask: An automated localization and segmentation method for leukocyte in blood smear images using deep neural networks

Digital pathology and microscope image analysis is widely used in comprehensive studies of cell morphology. Identification and analysis of leukocytes in blood smear images, acquired from bright field microscope, are vital for diagnosing many diseases such as hepatitis, leukaemia and acquired immune deficiency syndrome (AIDS). The major challenge for robust and accurate identification and segmentation of leukocyte in blood smear images lays in the large variations of cell appearance such as size, colour and shape of cells, the adhesion between leukocytes (white blood cells, WBCs) and erythrocytes (red blood cells, RBCs), and the emergence of substantial dyeing impurities in blood smear images. In this paper, an end‐to‐end leukocyte localization and segmentation method is proposed, named LeukocyteMask, in which pixel‐level prior information is utilized for supervisor training of a deep convolutional neural network, which is then employed to locate the region of interests (ROI) of leukocyte, and finally segmentation mask of leukocyte is obtained based on the extracted ROI by forward propagation of the network. Experimental results validate the effectiveness of the propose method and both the quantitative and qualitative comparisons with existing methods indicate that LeukocyteMask achieves a state‐of‐the‐art performance for the segmentation of leukocyte in terms of robustness and accuracy .     

Thin-section ratiometric Ca2+ images obtained by optical sectioning of fura-2 loaded mast cells

The availability of the ratiometric Ca2+ indicator dyes, fura-2, and indo-1, and advances in digital imaging and computer technology have made it possible to detect Ca2+ changes in single cells with high temporal and spatial resolution. However, the optical properties of the conventional epifluorescence microscope do not produce a perfect image of the specimen. Instead, the observed image is a spatial low pass filtered version of the object and is contaminated with out of focus information. As a result, the image has reduced contrast and an increased depth of field. This problem is especially important for measurements of localized Ca2+ concentrations. One solution to this problem is to use a scanning confocal microscope which only detects in focus information, but this approach has several disadvantages for low light fluorescence measurements in living cells. An alternative approach is to use digital image processing and a deblurring algorithm to remove the out of focus information by using a knowledge of the point spread function of the microscope. All of these algorithms require a stack of two-dimensional images taken at different focal planes, although the "nearest neighbor deblurring" algorithm only requires one image above and below the image plane. We have used a modification of this scheme to construct a simple inverse filter, which extracts optical sections comparable to those of the nearest neighbors scheme, but without the need for adjacent image sections. We have used this "no neighbors" processing scheme to deblur images of fura-2-loaded mast cells from beige mice and generate high resolution ratiometric Ca2+ images of thin sections through the cell. The shallow depth of field of these images is demonstrated by taking pairs of images at different focal planes, 0.5-microns apart. The secretory granules, which exclude the fura-2, appear in focus in all sections and distinct changes in their size and shape can be seen in adjacent sections. In addition, we show, with the aid of model objects, how the combination of inverse filtering and ratiometric imaging corrects for some of the inherent limitations of using an inverse filter and can be used for quantitative measurements of localized Ca2+ gradients. With this technique, we can observe Ca2+ transients in narrow regions of cytosol between the secretory granules and plasma membrane that can be less than 0.5-microns wide. Moreover, these Ca2+ increases can be seen to coincide with the swelling of the secretory granules that follows exocytotic fusion.     

On the Use of Luminescence Intensity Images for Quantified Characterization of Perovskite Solar Cells: Spatial Distribution of Series Resistance

Perovskite solar cells (PSCs) have made rapid advances in efficiency when fabricated as small‐area devices. A key challenge is to increase the active area while retaining high performance, which requires fast and reliable measurement techniques to spatially resolve cell properties. Luminescence imaging‐based techniques are one attractive possibility. A thermodynamic treatment of the luminescence radiation from MAPbI₃ and related perovskite semiconductors predicts that the intensity of luminescence emission is proportional to the electrochemical potential in the perovskite absorber, bringing with it numerous experimental advantages. However, concerns arise about the impact of the often‐observed hysteretic behavior on the interpretation of luminescence‐based measurements. This study demonstrates that despite their hysteretic phenomena, at steady‐state perovskite solar cells are amenable to quantitative analysis of luminescence images. This is demonstrated by calculating the spatial distribution of series resistance from steady‐state photoluminescence images. This study observes good consistency between the magnitude, voltage‐dependence, and spatial distribution of series resistance calculated from luminescence images and from cell‐level current–voltage curves and uncalibrated luminescence images, respectively. This method has significant value for the development of PSC process control, design and material selection, and illustrates the possibilities for large‐area, spatially resolved, quantitative luminescence imaging‐based characterization of PSCs.     

A quantitative analysis of the human bone marrow erythroblastic cell lineage using the SAMBA 200 cell image processor. I. The normal maturation sequence

A quantitative image analysis of the normal maturation sequence for the human bone marrow erythroblastic lineage was performed using the SAMBA 200 cell image processor. The different image analysis steps (image acquisition, preprocessing, segmentation, parametrization and data analysis) are briefly described. Thirty-three parameters related to geometry, color, texture and densitometry were computed on 638 cell images belonging to the five erythroblastic maturation stages. The automated classification of these cells, based upon a stepwise linear discriminant analysis, resulted in 80% correctly classified cells. Acceptance of confusions between successive maturation stages enhanced the rate of correctly classified cells to 100%. Among the ten most discriminating parameters, the nuclear area showed the highest correlation with the changes throughout the maturation process. The projection of the maturation sequence onto the factorial plane resulting from the canonical analysis emphasizes the existence of three phases of the maturation process, a finding that correlates well with the cytologic evolution and the biochemical and functional events during the maturation. The trajectory of cells within this factorial plane is thus regarded as a differentiation path from which a measure of the maturation could be derived.     

Determination of three-dimensional imaging properties of a light microscope system. Partial confocal behavior in epifluorescence microscopy.

We have determined the three-dimensional image-forming properties of an epifluorescence microscope for use in obtaining very high resolution three-dimensional images of biological structures by image processing methods. Three-dimensional microscopic data is collected as a series of two-dimensional images recorded at different focal planes. Each of these images contains not only in-focus information from the region around the focal plane, but also out-of-focus contributions from the remainder of the specimen. Once the imaging properties of the microscope system are characterized, powerful image processing methods can be utilized to remove the out-of-focus information and to correct for image distortions. Although theoretical calculations for the behavior of an aberration-free microscope system are available, the properties of real lenses under the conditions used for biological observation are often far from an ideal. For this reason, we have directly determined the image-forming properties of an epifluorescence microscope under conditions relevant to biological observations. Through-focus series of a point object (fluorescently-coated microspheres) were recorded on a charge-coupled device image detector. From these images, the three-dimensional point spread function and its Fourier transform, the optical transfer function, were derived. There were significant differences between the experimental results and the theoretical models which have important implications for image processing. The discrepancies can be explained by imperfections of the microscope system, nonideal observation conditions, and partial confocal effects found to occur with epifluorescence illumination. Understanding the optical behavior of the microscope system has indicated how to optimize specimen preparation, data collection, and processing protocols to obtain significantly improved images.     

Multispectral/hyperspectral image enhancement for biological cell analysis.

BACKGROUND: Microscopes form projected images from illuminated objects, such as cellular tissue, which are recorded at a distance through the optical system's field of view. A telescope on a satellite or airplane also forms images with a similar optical projection of objects on the ground. Typical visible illuminations form a displayed set of three-color channels (Red Green Blue [RGB]) that are combined from three image sensor arrays (e.g., focal plane arrays) into a single pixel coding for each color present in the image. Analysis of these RGB color images develops a qualitative image representation of the objects. METHODS: Independent component analysis (ICA) is used for analysis and enhancement of multispectral images, and compared with the similar and widely used principal component analysis. RESULTS: The data examples indicate that the ICA enhancement, and the resulting RGB image combination display, can be useful in processing datacubes of cellular data where isolation of unknown subtle image elements representing objects is desired. CONCLUSIONS: ICA image enhancement can aid processing of datacubes of cellular data by clarifying subtle image elements. These parallelizable algorithms can be implemented for real-time, online analysis.