Application of sensitive,high‐resolution imaging at a commercial lab‐based X‐ray micro‐CT system using propagation‐based phase retrieval

Several dedicated commercial lab‐based micro‐computed tomography (μCT) systems exist, which provide high‐resolution images of samples, with the capability to also deliver in‐line phase contrast. X‐ray phase contrast is particularly beneficial when visualizing very small features and weakly absorbing samples. The raw measured projections will include both phase and absorption effects. Extending our previous work that addressed the optimization of experimental conditions at the commercial ZEISS Xradia 500 Versa system, single‐distance phase‐contrast imaging is demonstrated on complex biological and material samples. From data captured at this system, we demonstrate extraction of the phase signal or the correction of the mixed image for the phase shift, and show how this procedure increases the contrast and removes artefacts. These high‐quality images, measured without the use of a synchrotron X‐ray source, demonstrate that highly sensitive, micrometre‐resolution imaging of 3D volumes is widely accessible using commercially advanced laboratory devices.     

An open‐source deconvolution software package for 3‐D quantitative fluorescence microscopy imaging

Deconvolution techniques have been widely used for restoring the 3‐D quantitative information of an unknown specimen observed using a wide‐field fluorescence microscope. Deconv , an open‐source deconvolution software package, was developed for 3‐D quantitative fluorescence microscopy imaging and was released under the GNU Public License. Deconv provides numerical routines for simulation of a 3‐D point spread function and deconvolution routines implemented three constrained iterative deconvolution algorithms: one based on a Poisson noise model and two others based on a Gaussian noise model. These algorithms are presented and evaluated using synthetic images and experimentally obtained microscope images, and the use of the library is explained. Deconv allows users to assess the utility of these deconvolution algorithms and to determine which are suited for a particular imaging application. The design of Deconv makes it easy for deconvolution capabilities to be incorporated into existing imaging applications.     

Strain mapping accuracy improvement using super‐resolution techniques

Super‐resolution (SR) software‐based techniques aim at generating a final image by combining several noisy frames with lower resolution from the same scene. A comparative study on high‐resolution high‐angle annular dark field images of InAs/GaAs QDs has been carried out in order to evaluate the performance of the SR technique. The obtained SR images present enhanced resolution and higher signal‐to‐noise (SNR) ratio and sharpness regarding the experimental images. In addition, SR is also applied in the field of strain analysis using digital image processing applications such as geometrical phase analysis and peak pairs analysis. The precision of the strain mappings can be improved when SR methodologies are applied to experimental images.     

Computer‐aided diagnosis software for vulvovaginal candidiasis detection from Pap smear images

Vulvovaginal candidiasis (VVC) is a common gynecologic infection and it occurs when there is overgrowth of the yeast called Candida. VVC diagnosis is usually done by observing a Pap smear sample under a microscope and searching for the conidium and mycelium components of Candida. This manual method is time consuming, subjective and tedious. Any diagnosis tools that detect VVC, semi‐ or full‐automatically, can be very helpful to pathologists. This article presents a computer aided diagnosis (CAD) software to improve human diagnosis of VVC from Pap smear samples. The proposed software is designed based on phenotypic and morphology features of the Candida in Pap smear sample images. This software provide a user‐friendly interface which consists of a set of image processing tools and analytical results that helps to detect Candida and determine severity of illness. The software was evaluated on 200 Pap smear sample images and obtained specificity of 91.04% and sensitivity of 92.48% to detect VVC. As a result, the use of the proposed software reduces diagnostic time and can be employed as a second objective opinion for pathologists.     

Metallization and Ar‐O plasma effects on dental enamel roughness evaluated with SEM and MeX™ for 3D reconstruction

The MeX? software is a useful tool for tridimensional data collection for surface evaluation and could be relevant to evaluate the same specimen in different phases of the study, assuming repeated measures of dental enamel roughness. The aim of this study was to evaluate the influence of sample metallization for dental enamel roughness analysis with 3D images reconstructed using MeX? software from Scanning Electron Microscopy (SEM) images. The influence of 74.98% (%mol/mol) argon?oxygen plasma for carbon layer removal on surface roughness of the metallized specimen was also evaluated. Dental enamel specimens were prepared for SEM analysis with and without carbon metallization using conventional or environmental modes. Argon?oxygen plasma for carbon layer removal was used and surface roughness was re‐evaluated. Roughness obtained by SEM and MeX? reconstructed images, with or without metallization, did not differ. No significant alteration on surface roughness after carbon layer removal using plasma was found. SEM baseline evaluation using conventional mode without sample preparation and in environmental mode were not comparable. Roughness of enamel 3D images reconstructed with MeX? software from SEM images, with or without metallization was similar. The 74.98% (%mol/mol) argon?oxygen plasma removed the carbon layer with no effect on enamel roughness.     

Ultrastructural and three‐dimensional study of post‐LASIK ectasia cornea

INTRODUCTION: Post‐laser in situ keratomileusis (LASIK) corneal ectasia is a serious late postoperative complication. Here, we report the ultrastructural features of the post‐LASIK cornea of two patients. METHODS: Two normal corneas (age 24 and 37 years old) and two post‐LASIK ectaic corneas from two patients (A and B) were studied. The “patient A” (age 27 years) underwent penetrating keratoplasty and “patient B” (age 31 years) underwent deep‐anterior lamellar keratoplasty. The excised corneas were processed for light and electron microscopy. A total of 120 images for three‐dimensional (3D) reconstruction were taken by using the software “Recorder” and using a bottom mounted camera “Quemesa” attached to a JOEL 1400 transmission electron microscope. The 3D images were constructed using “Visual Kai” software. RESULTS: In the post‐LASIK cornea, the hemidesmosomes, the basement membrane, and Bowman”s layer were abnormal. The stromal lamellae were thin and disorganized. The collagen fibrils (CFs) diameter and interfibrillar spacing had decreased. Aggregated microfibrils were present in the Bowman's layer and all parts of the stroma. A large number of microfilaments were present at the detachment end of the flap and residual stroma. The 3D images showed the presence of collagen microfibrils and proteoglycans (PGs) within the CF of the normal and post‐LASIK cornea. The collagen microfibrils and PGs within the CFs had degenerated in the post‐LASIK cornea. CONCLUSION: Collagen microfibrils and PGs within the CFs were degenerated, leading to the degeneration of CFs, followed by the disorganization of lamellae in post‐LASIK cornea. The CFs diameter and interfibrillar spacing decreased. Microsc. Res. Tech. 77:91–98, 2014. © 2013 Wiley Periodicals, Inc.     

An automatic vision‐based malaria diagnosis system

Malaria is a worldwide health problem with 225 million infections each year. A fast and easy‐to‐use method, with high performance is required to differentiate malaria from non‐malarial fevers. Manual examination of blood smears is currently the gold standard, but it is time‐consuming, labour‐intensive, requires skilled microscopists and the sensitivity of the method depends heavily on the skills of the microscopist. We propose an easy‐to‐use, quantitative cartridge‐scanner system for vision‐based malaria diagnosis, focusing on low malaria parasite densities. We have used special finger‐prick cartridges filled with acridine orange to obtain a thin blood film and a dedicated scanner to image the cartridge. Using supervised learning, we have built a Plasmodium falciparum detector. A two‐step approach was used to first segment potentially interesting areas, which are then analysed in more detail. The performance of the detector was validated using 5 420 manually annotated parasite images from malaria parasite culture in medium, as well as using 40 cartridges of 11 780 images containing healthy blood. From finger prick to result, the prototype cartridge‐scanner system gave a quantitative diagnosis in 16 min, of which only 1 min required manual interaction of basic operations. It does not require a wet lab or a skilled operator and provides parasite images for manual review and quality control. In healthy samples, the image analysis part of the system achieved an overall specificity of 99.999978% at the level of (infected) red blood cells, resulting in at most seven false positives per microlitre. Furthermore, the system showed a sensitivity of 75% at the cell level, enabling the detection of low parasite densities in a fast and easy‐to‐use manner. A field trial in Chittagong (Bangladesh) indicated that future work should primarily focus on improving the filling process of the cartridge and the focus control part of the scanner.     

Reduction of coherent artefacts in super‐resolution fluorescence localisation microscopy

Super‐resolution localisation microscopy techniques depend on uniform illumination across the field of view, otherwise the resolution is degraded, resulting in imaging artefacts such as fringes. Lasers are currently the light source of choice for switching fluorophores in PALM/STORM methods due to their high power and narrow bandwidth. However, the high coherence of these sources often creates interference phenomena in the microscopes, with associated fringes/speckle artefacts in the images. We quantitatively demonstrate the use of a polymer membrane speckle scrambler to reduce the effect of the coherence phenomena. The effects of speckle in the illumination plane, at the camera and after software localisation of the fluorophores, were characterised. Speckle phenomena degrade the resolution of the microscope at large length scales in reconstructed images, effects that were suppressed by the speckle scrambler, but the small length scale resolution is unchanged at ～30 nm.     

Registration of phase‐contrast images in propagation‐based X‐ray phase tomography

X‐ray phase tomography aims at reconstructing the 3D electron density distribution of an object. It offers enhanced sensitivity compared to attenuation‐based X‐ray absorption tomography. In propagation‐based methods, phase contrast is achieved by letting the beam propagate after interaction with the object. The phase shift is then retrieved at each projection angle, and subsequently used in tomographic reconstruction to obtain the refractive index decrement distribution, which is proportional to the electron density. Accurate phase retrieval is achieved by combining images at different propagation distances. For reconstructions of good quality, the phase‐contrast images recorded at different distances need to be accurately aligned. In this work, we characterise the artefacts related to misalignment of the phase‐contrast images, and investigate the use of different registration algorithms for aligning in‐line phase‐contrast images. The characterisation of artefacts is done by a simulation study and comparison with experimental data. Loss in resolution due to vibrations is found to be comparable to attenuation‐based computed tomography. Further, it is shown that registration of phase‐contrast images is nontrivial due to the difference in contrast between the different images, and the often periodical artefacts present in the phase‐contrast images if multilayer X‐ray optics are used. To address this, we compared two registration algorithms for aligning phase‐contrast images acquired by magnified X‐ray nanotomography: one based on cross‐correlation and one based on mutual information. We found that the mutual information‐based registration algorithm was more robust than a correlation‐based method.     

Pipeline for illumination correction of images for high‐throughput microscopy

The presence of systematic noise in images in high‐throughput microscopy experiments can significantly impact the accuracy of downstream results. Among the most common sources of systematic noise is non‐homogeneous illumination across the image field. This often adds an unacceptable level of noise, obscures true quantitative differences and precludes biological experiments that rely on accurate fluorescence intensity measurements. In this paper, we seek to quantify the improvement in the quality of high‐content screen readouts due to software‐based illumination correction. We present a straightforward illumination correction pipeline that has been used by our group across many experiments. We test the pipeline on real‐world high‐throughput image sets and evaluate the performance of the pipeline at two levels: (a) Z′‐factor to evaluate the effect of the image correction on a univariate readout, representative of a typical high‐content screen, and (b) classification accuracy on phenotypic signatures derived from the images, representative of an experiment involving more complex data mining. We find that applying the proposed post‐hoc correction method improves performance in both experiments, even when illumination correction has already been applied using software associated with the instrument. To facilitate the ready application and future development of illumination correction methods, we have made our complete test data sets as well as open‐source image analysis pipelines publicly available. This software‐based solution has the potential to improve outcomes for a wide‐variety of image‐based HTS experiments.     

Three‐dimensional imaging of whole mouse models: comparing nondestructive X‐ray phase‐contrast micro‐CT with cryotome‐based planar epi‐illumination imaging

In this study, we compare two evolving techniques for obtaining high‐resolution 3D anatomical data of a mouse specimen. On the one hand, we investigate cryotome‐based planar epi‐illumination imaging (cryo‐imaging). On the other hand, we examine X‐ray phase‐contrast micro‐computed tomography (micro‐CT) using synchrotron radiation. Cryo‐imaging is a technique in which an electron multiplying charge coupled camera takes images of a cryo‐frozen specimen during the sectioning process. Subsequent image alignment and virtual stacking result in volumetric data. X‐ray phase‐contrast imaging is based on the minute refraction of X‐rays inside the specimen and features higher soft‐tissue contrast than conventional, attenuation‐based micro‐CT. To explore the potential of both techniques for studying whole mouse disease models, one mouse specimen was imaged using both techniques. Obtained data are compared visually and quantitatively, specifically with regard to the visibility of fine anatomical details. Internal structure of the mouse specimen is visible in great detail with both techniques and the study shows in particular that soft‐tissue contrast is strongly enhanced in the X‐ray phase images compared to the attenuation‐based images. This identifies phase‐contrast micro‐CT as a powerful tool for the study of small animal disease models.     

Resolution and super‐resolution

Many papers have claimed the attainment of super‐resolution, i.e. resolution beyond that achieved classically, by measurement of the profile of a feature in the image. We argue that measurement of the contrast of the image of a dark bar on a bright background does not give a measure of resolution, but of detection sensitivity. The width of a bar that gives an intensity at the center of the bar of 0.735 that in the bright region (the same ratio as in the Rayleigh resolution criterion) is for the coherent case with central illumination. This figure, which compares with for the Abbe resolution limit with central illumination, holds for the classical case, and so is no indication of super‐resolution. Theoretical images for two points, two lines, arrays of lines, arrays of bars, and grating objects are compared. These results can be used a reference for experimental results, to determine if super‐resolution has indeed been attained. The history of the development of the theory of microscope resolution is outlined.     

Full supervised learning for osteoporosis diagnosis using micro‐CT images

Early osteoporosis diagnosis is of important significance for reducing fracture risk. Image analysis provides a new perspective for noninvasive diagnosis in recent years. In this article, we propose a novel method based on machine‐learning method performed on micro‐CT images todiagnose osteoporosis. The aim of this work is to find a way to more effectively and accurately diagnose osteoporosis on which many methods have been proposed and practiced. In this method, in contrast to the previously proposed methods in which features are analyzed individually, several features are combined to build a classifier for distinguishing osteoporosis group and normal group. Twelve features consisting of two groups are involved in our research, including bone volume/total volume (BV/TV), bone surface/bone volume (BS/BV), trabecular number (Tb.N), obtained from the software of micro‐CT, and other four features from volumetric topological analysis (VTA). Support vector machine (SVM) method and k‐nearest neighbor (kNN) method are introduced to create classifiers with these features due to their excellent performances on classification. In the experiment, 200 micro‐CT images are used in which half are from osteoporosis patients and the rest are from normal people. The performance of the obtained classifiers is evaluated by precision, recall, and F‐measure. The best performance with precision of 100%, recall of 100%, and F‐measure of 100% is acquired when all the features are included. The satisfying result demonstrates that SVM and kNN are effective for diagnosing osteoporosis with micro‐CT images. Microsc. Res. Tech. 76:333–341, 2013. © 2013 Wiley Periodicals, Inc.     

Out‐of‐focus background subtraction for fast structured illumination super‐resolution microscopy of optically thick samples

We propose a structured illumination microscopy method to combine super resolution and optical sectioning in three‐dimensional (3D) samples that allows the use of two‐dimensional (2D) data processing. Indeed, obtaining super‐resolution images of thick samples is a difficult task if low spatial frequencies are present in the in‐focus section of the sample, as these frequencies have to be distinguished from the out‐of‐focus background. A rigorous treatment would require a 3D reconstruction of the whole sample using a 3D point spread function and a 3D stack of structured illumination data. The number of raw images required, 15 per optical section in this case, limits the rate at which high‐resolution images can be obtained. We show that by a succession of two different treatments of structured illumination data we can estimate the contrast of the illumination pattern and remove the out‐of‐focus content from the raw images. After this cleaning step, we can obtain super‐resolution images of optical sections in thick samples using a two‐beam harmonic illumination pattern and a limited number of raw images. This two‐step processing makes it possible to obtain super resolved optical sections in thick samples as fast as if the sample was two‐dimensional.     

Alignment with sub‐pixel accuracy for images of multi‐modality microscopes using automatic calibration

A biological specimen is often imaged with various imaging modalities, and it is crucial that such images are well aligned to best reveal physiological structures and functions of the specimen for in‐depth analyses. In this paper, we present a methodology for automatic calibration of multiple optical imaging modalities within the x–y detector plane using a custom chrome‐on‐glass target and an automatic and accurate registration algorithm. The target contains lines crossing at random angles, and our method of registration is based on the alignment of salient features extracted from the lines within the individual images. Once spatial relationships are found between the various detectors and applied to the resultant images, no further registration is required for all static samples, and the registered images serve as the starting point for registration of dynamic samples, where the remaining misalignment is caused by sample movement. We have validated our algorithm with 40 inter‐modal and 30 intra‐modal image pairs, and the success rates are 95 and 100%, respectively, with sub‐pixel accuracy. This methodology is widely applicable to any multi‐modal microscope that combines a number of imaging modalities on a common platform assuming images of the target can be obtained.     

Real‐space observation of strong metal‐support interaction: state‐of‐the‐art and what's the next

The real‐space resolving of the encapsulated overlayer in the well‐known model and industry catalysts, ascribed to the advent of dedicated transmission electron microscopy, enables us to probe novel nano/micro architecture chemistry for better application, revisiting our understanding of this key issue in heterogeneous catalysis. In this review, we summarize the latest progress of real‐space observation of SMSI in several well‐known systems mainly covered from the metal catalysts (mostly Pt) supported by the TiO₂, CeO₂ and Fe₃O₄. As a comparison with the model catalyst Pt/Fe₃O₄, the industrial catalyst Cu/ZnO is also listed, followed with the suggested ongoing directions in the field.     

Perceptual clustering for automatic hotspot detection from Ki‐67‐stained neuroendocrine tumour images

Hotspot detection plays a crucial role in grading of neuroendocrine tumours of the digestive system. Hotspots are often detected manually from Ki‐67‐stained images, a practice which is tedious, irreproducible and error prone. We report a new method to segment Ki‐67‐positive nuclei from Ki‐67‐stained slides of neuroendocrine tumours. The method combines minimal graph cuts along with the multistate difference of Gaussians to detect the individual cells from images of Ki‐67‐stained slides. It, then, automatically defines the composite function, which is used to determine hotspots in neuroendocrine tumour slide images. We combine modified particle swarm optimization with message passing clustering to mimic the thought process of the pathologist during hotspot detection in neuroendocrine tumour slide images. The proposed method was tested on 55 images of size 10 × 5 K and resulted in an accuracy of 94.60%. The developed methodology can also be part of the workflow for other diseases such as breast cancer and glioblastomas.     

Reducing scanning electron microscope charging by using exponential contrast stretching technique on post‐processing images

An exponential contrast stretching (ECS) technique is developed to reduce the charging effects on scanning electron microscope images. Compared to some of the conventional histogram equalization methods, such as bi‐histogram equalization and recursive mean‐separate histogram equalization, the proposed ECS method yields better image compensation. Diode sample chips with insulating and conductive surfaces are used as test samples to evaluate the efficiency of the developed algorithm. The algorithm is implemented in software with a frame grabber card, forming the front‐end video capture element.     

AutoIHC‐scoring: a machine learning framework for automated Allred scoring of molecular expression in ER‐ and PR‐stained breast cancer tissue

In prognostic evaluation of breast cancer Immunohistochemical (IHC) markers namely, oestrogen receptor (ER) and progesterone receptor (PR) are widely used. The expert pathologist investigates qualitatively the stained tissue slide under microscope to provide the Allred score; which is clinically used for therapeutic decision making. Such qualitative judgment is time‐consuming, tedious and more often suffers from interobserver variability. As a result, it leads to imprecise IHC score for ER and PR. To overcome this, there is an urgent need of developing a reliable and efficient IHC quantifier for high throughput decision making. In view of this, our study aims at developing an automated IHC profiler for quantitative assessment of ER and PR molecular expression from stained tissue images. We propose here to use CMYK colour space for positively and negatively stained cell extraction for proportion score. Also colour features are used for quantitative assessment of intensity scoring among the positively stained cells. Five different machine learning models namely artificial neural network, Naïve Bayes, K‐nearest neighbours, decision tree and random forest are considered for learning the colour features using average red, green and blue pixel values of positively stained cell patches. Fifty cases of ER‐ and PR‐stained tissues have been evaluated for validation with the expert pathologist's score. All five models perform adequately where random forest shows the best correlation with the expert's score (Pearson's correlation coefficient = 0.9192). In the proposed approach the average variation of diaminobenzidine (DAB) to nuclear area from the expert's score is found to be 7.58%, as compared to 27.83% for state‐of‐the‐art ImmunoRatio software.     

Background intensity correction for terabyte‐sized time‐lapse images

Several computational challenges associated with large‐scale background image correction of terabyte‐sized fluorescent images are discussed and analysed in this paper. Dark current, flat‐field and background correction models are applied over a mosaic of hundreds of spatially overlapping fields of view (FOVs) taken over the course of several days, during which the background diminishes as cell colonies grow. The motivation of our work comes from the need to quantify the dynamics of OCT‐4 gene expression via a fluorescent reporter in human stem cell colonies. Our approach to background correction is formulated as an optimization problem over two image partitioning schemes and four analytical correction models. The optimization objective function is evaluated in terms of (1) the minimum root mean square (RMS) error remaining after image correction, (2) the maximum signal‐to‐noise ratio (SNR) reached after downsampling and (3) the minimum execution time. Based on the analyses with measured dark current noise and flat‐field images, the most optimal GFP background correction is obtained by using a data partition based on forming a set of submosaic images with a polynomial surface background model. The resulting image after correction is characterized by an RMS of about 8, and an SNR value of a 4 × 4 downsampling above 5 by Rose criterion. The new technique generates an image with half RMS value and double SNR value when compared to an approach that assumes constant background throughout the mosaic. We show that the background noise in terabyte‐sized fluorescent image mosaics can be corrected computationally with the optimized triplet (data partition, model, SNR driven downsampling) such that the total RMS value from background noise does not exceed the magnitude of the measured dark current noise. In this case, the dark current noise serves as a benchmark for the lowest noise level that an imaging system can achieve. In comparison to previous work, the past fluorescent image background correction methods have been designed for single FOV and have not been applied to terabyte‐sized images with large mosaic FOVs, low SNR and diminishing access to background information over time as cell colonies span entirely multiple FOVs. The code is available as open‐source from the following link https://isg.nist.gov/ .