Three‐dimensional visualization of dermal skin structure using confocal laser scanning microscopy

The properties and performance of collagen‐based materials may be affected by the collagen fibre bundle pattern, orientation and weave. The aim of this study was to develop and apply methods to visualize the dermis using confocal laser scanning microscopy from thin tissue sections stained with haematoxylin and eosin. The data was processed to allow three‐dimensional (3‐D) visualization on a PC and using a 3‐D immersive technology system. The 3‐D visualization of the confocal microscope image stacks allowed the evaluation of the collagen macromolecular structure including the collagen fibre bundles. The methods developed provide a novel way of viewing complex organic structures with further potential applications in the medical field.     

Parallel deconvolution of large 3D images obtained by confocal laser scanning microscopy

Various deconvolution algorithms are often used for restoration of digital images. Image deconvolution is especially needed for the correction of three‐dimensional images obtained by confocal laser scanning microscopy. Such images suffer from distortions, particularly in the Z dimension. As a result, reliable automatic segmentation of these images may be difficult or even impossible. Effective deconvolution algorithms are memory‐intensive and time‐consuming. In this work, we propose a parallel version of the well‐known Richardson–Lucy deconvolution algorithm developed for a system with distributed memory and implemented with the use of Message Passing Interface (MPI). It enables significantly more rapid deconvolution of two‐dimensional and three‐dimensional images by efficiently splitting the computation across multiple computers. The implementation of this algorithm can be used on professional clusters provided by computing centers as well as on simple networks of ordinary PC machines. Microsc. Res. Tech., 2010. © 2009 Wiley‐Liss, Inc.     

A method for the evaluation of thousands of automated 3D stem cell segmentations

There is no segmentation method that performs perfectly with any dataset in comparison to human segmentation. Evaluation procedures for segmentation algorithms become critical for their selection. The problems associated with segmentation performance evaluations and visual verification of segmentation results are exaggerated when dealing with thousands of three‐dimensional (3D) image volumes because of the amount of computation and manual inputs needed. We address the problem of evaluating 3D segmentation performance when segmentation is applied to thousands of confocal microscopy images (z‐stacks). Our approach is to incorporate experimental imaging and geometrical criteria, and map them into computationally efficient segmentation algorithms that can be applied to a very large number of z‐stacks. This is an alternative approach to considering existing segmentation methods and evaluating most state‐of‐the‐art algorithms. We designed a methodology for 3D segmentation performance characterization that consists of design, evaluation and verification steps. The characterization integrates manual inputs from projected surrogate ‘ground truth’ of statistically representative samples and from visual inspection into the evaluation. The novelty of the methodology lies in (1) designing candidate segmentation algorithms by mapping imaging and geometrical criteria into algorithmic steps, and constructing plausible segmentation algorithms with respect to the order of algorithmic steps and their parameters, (2) evaluating segmentation accuracy using samples drawn from probability distribution estimates of candidate segmentations and (3) minimizing human labour needed to create surrogate ‘truth’ by approximating z‐stack segmentations with 2D contours from three orthogonal z‐stack projections and by developing visual verification tools. We demonstrate the methodology by applying it to a dataset of 1253 mesenchymal stem cells. The cells reside on 10 different types of biomaterial scaffolds, and are stained for actin and nucleus yielding 128 460 image frames (on average, 125 cells/scaffold × 10 scaffold types × 2 stains × 51 frames/cell). After constructing and evaluating six candidates of 3D segmentation algorithms, the most accurate 3D segmentation algorithm achieved an average precision of 0.82 and an accuracy of 0.84 as measured by the Dice similarity index where values greater than 0.7 indicate a good spatial overlap. A probability of segmentation success was 0.85 based on visual verification, and a computation time was 42.3 h to process all z‐stacks. While the most accurate segmentation technique was 4.2 times slower than the second most accurate algorithm, it consumed on average 9.65 times less memory per z‐stack segmentation.     

Algorithms for accurate 3D registration of neuronal images acquired by confocal scanning laser microscopy

This paper presents automated and accurate algorithms based on high‐order transformation models for registering three‐dimensional (3D) confocal images of dye‐injected neurons. The algorithms improve upon prior methods in several ways, and meet the more stringent image registration needs of applications such as two‐view attenuation correction recently developed by us. First, they achieve high accuracy (≈ 1.2 voxels, equivalent to 0.4 µm) by using landmarks, rather than intensity correlations, and by using a high‐dimensional affine and quadratic transformation model that accounts for 3D translation, rotation, non‐isotropic scaling, modest curvature of field, distortions and mechanical inconsistencies introduced by the imaging system. Second, they use a hierarchy of models and iterative algorithms to eliminate potential instabilities. Third, they incorporate robust statistical methods to achieve accurate registration in the face of inaccurate and missing landmarks. Fourth, they are fully automated, even estimating the initial registration from the extracted landmarks. Finally, they are computationally efficient, taking less than a minute on a 900‐MHz Pentium III computer for registering two images roughly 70 MB in size. The registration errors represent a combination of modelling, estimation, discretization and neuron tracing errors. Accurate 3D montaging is described; the algorithms have broader applicability to images of vasculature, and other structures with distinctive point, line and surface landmarks.     

Two‐dimensional structured illumination microscopy

In widefield fluorescence microscopy, images from all but very flat samples suffer from fluorescence emission from layers above or below the focal plane of the objective lens. Structured illumination microscopy provides an elegant approach to eliminate this unwanted image contribution. To this end a line grid is projected onto the sample and phase images are taken at different positions of the line grid. Using suitable algorithms ‘quasi‐confocal images’ can be derived from a given number of such phase‐images. Here, we present an alternative structured illumination microscopy approach, which employs two‐dimensional patterns instead of a one‐dimensional one. While in one‐dimensional structured illumination microscopy the patterns are shifted orthogonally to the pattern orientation, in our two‐dimensional approach it is shifted at a single, pattern‐dependent angle, yet it already achieves an isotropic power spectral density with this unidirectional shift, which otherwise would require a combination of pattern‐shift and ‐rotation. Moreover, our two‐dimensional approach also yields a better signal‐to‐noise ratio in the evaluated image.     

Usefulness of hard X‐ray microscope using synchrotron radiation for the structure analysis of insects

Three‐dimensional (3D) printing technology has the advantage of enabling specific visualization of creative ideas. Since synchrotron based images can provide high sensitivity and high resolution, they are a very useful technology to identify the 3D anatomy of microscale samples. X‐ray images using such synchrotron radiation are grafted to 3D printing technology. We can be obtained 3D images and modeling data of an ant using synchrotron radiation, and then, it were outputted with the 3D printer. A new way to identify the usefulness of the structure analysis is then found by visualizing the micro‐internal structure of diverse biomedical samples and creating an enlarged model. This study suggests methods of utilizing a 3D printed model produced through synchrotron radiation imaging in various fields such as bioengineering, medical, and education.     

Three-dimensional computer reconstruction of large tissue volumes based on composing series of high-resolution confocal images by GlueMRC and LinkMRC software

Computer-based visualization of large tissue volumes with high resolution based on composing series of high-resolution confocal images is presented. GlueMRC and LinkMRC programs are introduced, implementing composition of overlapping series of optical sections captured by a confocal microscope, registration and subsequent composition of successive confocal stacks. Both programs are using an interactive approach in combination with automatic algorithms for image registration. Further, the method for obtaining surface renderings of microscopical structure under study is described. For this purpose, structure contours visible in the sections are interactively digitized using a Colon plug-in module running in Ellipse environment. Then the coordinates of the contours are processed by special modules in the graphic programming environment IRIS Explorer and the structure surface is rendered. The method is shown on the 3-D reconstruction of the capillary bed of human placental villi and chick embryonic gut and its vascular bed.     

Three‐dimensional microscopy data exploration by interactive volume visualization

This paper presents a new volume visualization approach for three‐dimensional (3‐D) interactive microscopy data exploration. Because of their unique image characteristics, 3‐D microscopy data are often not able to be visualized effectively by conventional volume visualization techniques. In our approach, microscopy visualization is carried out in an interactive data exploration environment, based on a combination of interactive volume rendering techniques and image‐based transfer function design methods. Interactive volume rendering is achieved by using two‐dimensional (2‐D) texture mapping in a Shear‐Warp volume rendering algorithm. Image processing techniques are employed and integrated into the rendering pipeline for the definition and searching of appropriate transfer functions that best reflect the user's visualization intentions. These techniques have been implemented successfully in a prototype visualization system on low‐end and middle‐range SGI desktop workstations. Since only 2‐D texture mapping is required, the system can also be easily ported to PC platforms.     

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.     

Three-dimensional volume reconstruction of extracellular matrix proteins in uveal melanoma from fluorescent confocal laser scanning microscope images

The distribution of looping patterns of laminin in uveal melanomas and other tumours has been associated with adverse outcome. Moreover, these patterns are generated by highly invasive tumour cells through the process of vasculogenic mimicry and are not therefore blood vessels. Nevertheless, these extravascular matrix patterns conduct plasma. The three‐dimensional (3D) configuration of these laminin‐rich patterns compared with blood vessels has been the subject of speculation and intensive investigation. We have developed a method for the 3D reconstruction of volume for these extravascular matrix proteins from serial paraffin sections cut at 4 µm thicknesses and stained with a fluorescently labelled antibody to laminin ( Maniotis et al., 2002 ). Each section was examined via confocal laser‐scanning focal microscopy (CLSM) and 13 images were recorded in the Z‐dimension for each slide. The input CLSM imagery is composed of a set of 3D subvolumes (stacks of 2D images) acquired at multiple confocal depths, from a sequence of consecutive slides. Steps for automated reconstruction included (1) unsupervised methods for selecting an image frame from a subvolume based on entropy and contrast criteria, (2) a fully automated registration technique for image alignment and (3) an improved histogram equalization method that compensates for spatially varying image intensities in CLSM imagery due to photo‐bleaching. We compared image alignment accuracy of a fully automated method with registration accuracy achieved by human subjects using a manual method. Automated 3D volume reconstruction was found to provide significant improvement in accuracy, consistency of results and performance time for CLSM images acquired from serial paraffin sections.     

Micro‐CT versus synchrotron radiation phase contrast imaging of human cochlea

High‐resolution images of the cochlea are used to develop atlases to extract anatomical features from low‐resolution clinical computed tomography (CT) images. We compare visualization and contrast of conventional absorption‐based micro‐CT to synchrotron radiation phase contrast imaging (SR‐PCI) images of whole unstained, nondecalcified human cochleae. Three cadaveric cochleae were imaged using SR‐PCI and micro‐CT. Images were visually compared and contrast‐to‐noise ratios (CNRs) were computed from n = 27 regions‐of‐interest (enclosing soft tissue) for quantitative comparisons. Three‐dimensional (3D) models of cochlear internal structures were constructed from SR‐PCI images using a semiautomatic segmentation method. SR‐PCI images provided superior visualization of soft tissue microstructures over conventional micro‐CT images. CNR improved from 7.5 ± 2.5 in micro‐CT images to 18.0 ± 4.3 in SR‐PCI images (p < 0.0001). The semiautomatic segmentations yielded accurate reconstructions of 3D models of the intracochlear anatomy. The improved visualization, contrast and modelling achieved using SR‐PCI images are very promising for developing atlas‐based segmentation methods for postoperative evaluation of cochlear implant surgery.     

Automated quantification and reconstruction of collagen matrix from 3D confocal datasets

The geometrical structure of fibrous extracellular matrix (ECM) impacts on its biological function. In this report, we demonstrate a new algorithm designed to extract quantitative structural information about individual collagen fibres (orientation, length and diameter) from 3D backscattered‐light confocal images of collagen gels. The computed quantitative data allowed us to create surface‐rendered 3D images of the investigated sample.     

Three‐dimensional visualization of rat retina by X‐ray differential phase contrast tomographic microscopy

The retina is one of the most tiny and sophisticated tissues of the body. Three dimensional (3D) visualization of the whole retina is valuable both in clinical and research arenas. The tissue has been predominantly assessed by time‐consuming histopathology and optical coherence tomography (OCT) in research and clinical arenas. However, none of the two methods can provide 3D imaging of the retina. The purpose of this study is to give a volumetric visualization of rat retina at submicron resolution, using an emerging imaging technique‐phase‐contrast X‐ray CT. A Sprague‐Dawley (SD) rat eye specimen was scanned with X‐ray differential phase contrast tomographic microscopy (DPC‐microCT) equipped at the Swiss Light Source synchrotron. After scanning, the specimen was subjected to routine histology procedures and severed as a reference. The morphological characteristics and signal features of the retina in the DPC‐microCT images were evaluated. The total retina and its sublayers thicknesses were measured on the DPC‐microCT images and compared with those obtained from the histological sections. The retina structures revealed by DPC‐microCT were highly consistent with the histological section. In this study, we achieved nondestructive 3D visualization of SD rat retina. In addition to detailed anatomical structures, the objective parameters provided by DPC‐microCT make it a useful tool for retinal research and disease diagnosis in the early stage.     

In vivo three-dimensional imaging of plants with optical coherence microscopy

Achieving the ability to non‐destructively, non‐invasively examine subsurface features of living multicellular organisms at a microscopic level is currently a challenge for biologists. Optical coherence microscopy (OCM) is a new photonics‐based technology that can be used to address this challenge. OCM takes advantage of refractive properties of biological molecules to generate three‐dimensional images that can be viewed with a computer. We describe new data processing techniques and a different visualization algorithm that substantially improve OCM images. We have applied OCM imaging, in conjunction with these improvements, to a variety of structures of plants, including leaves, flowers, ovules and germinating seeds, and describe the visualization of cellular and subcellular structures within intact plants. We present evidence, based on detailed examination of our OCM images, comparisons to classical plant anatomy studies, and current knowledge of light scattering by cells and their components, that we can distinguish nuclei, organelles and vacuoles. Detailed examination of vascular tissue, which contains cells with elaborate wall structure, shows that cell walls produce no significant OCM signal. These improvements to the visualization process, together with the powerful non‐invasive, non‐destructive aspects of the technology, will broaden the application of OCM to questions in studies of plants as well as animals.     

Dimensional metrology of lab‐on‐a‐chip internal structures: a comparison of optical coherence tomography with confocal fluorescence microscopy

The characterization of internal structures in a polymeric microfluidic device, especially of a final product, will require a different set of optical metrology tools than those traditionally used for microelectronic devices. We demonstrate that optical coherence tomography (OCT) imaging is a promising technique to characterize the internal structures of poly(methyl methacrylate) devices where the subsurface structures often cannot be imaged by conventional wide field optical microscopy. The structural details of channels in the devices were imaged with OCT and analyzed with an in‐house written ImageJ macro in an effort to identify the structural details of the channel. The dimensional values obtained with OCT were compared with laser‐scanning confocal microscopy images of channels filled with a fluorophore solution. Attempts were also made using confocal reflectance and interferometry microscopy to measure the channel dimensions, but artefacts present in the images precluded quantitative analysis. OCT provided the most accurate estimates for the channel height based on an analysis of optical micrographs obtained after destructively slicing the channel with a microtome. OCT may be a promising technique for the future of three‐dimensional metrology of critical internal structures in lab‐on‐a‐chip devices because scans can be performed rapidly and noninvasively prior to their use.     

Depth enhancement of 3D microscopic living‐cell image using incoherent fluorescent digital holography

Multilayer images of living cells are typically obtained using confocal or multiphoton microscopy. However, limitations on the distance between consecutive scan layers hinder high‐resolution three‐dimensional reconstruction, and scattering strongly degrades images of living cell components. Consequently, when overlapping information from different layers is focused on a specific point in the camera, this causes uncertainty in the depiction of the cell components. We propose a method that combines the Fresnel incoherent correlation holography and a depth‐of‐focus reduction algorithm to enhance the depth information of three‐dimensional cell images. The proposed method eliminates overlap between light elements in the different layers inside living cells and limitations on the interlayer distance, and also enhances the contrast of the reconstructed holograms of living cells.     

MRT letter: Extended depth from focus reconstruction method for stretch zone measurement in 15-5PH steel

The stretch zone width (SZW) data for 15‐5PH steel CTOD specimens fractured at ?150°C to + 23°C temperature were measured based on focused images and 3D maps obtained by extended depth‐of‐field reconstruction from light microscopy (LM) image stacks. This LM‐based method, with a larger lateral resolution, seems to be as effective for quantitative analysis of SZW as scanning electron microscopy (SEM) or confocal scanning laser microscopy (CSLM), permitting to clearly identify stretch zone boundaries. Despite the worst sharpness of focused images, a robust linear correlation was established to fracture toughness (K_C) and SZW data for the 15‐5PH steel tested specimens, measured at their center region. The method is an alternative to evaluate the boundaries of stretched zones, at a lower cost of implementation and training, since topographic data from elevation maps can be associated with reconstructed image, which summarizes the original contrast and brightness information. Finally, the extended depth‐of‐field method is presented here as a valuable tool for failure analysis, as a cheaper alternative to investigate rough surfaces or fracture, compared to scanning electron or confocal light microscopes. Microsc. Res. Tech. 75:1155–1158, 2012. © 2012 Wiley Periodicals, Inc.     

Automated high-throughput electron tomography by pre-calibration of image shifts

Electron tomography is a versatile method for obtaining three‐dimensional (3D) images with transmission electron microscopy. The technique is suitable to investigate cell organelles and tissue sections (100–500 nm thick) with 4–20 nm resolution. 3D reconstructions are obtained by processing a series of images acquired with the samples tilted over different angles. While tilting the sample, image shifts and defocus changes of several µm can occur. The current generation of automated acquisition software detects and corrects for these changes with a procedure that incorporates switching the electron optical magnification. We developed a novel method for data collection based on the measurement of shifts prior to data acquisition, which results in a five‐fold increase in speed, enabling the acquisition of 151 images in less than 20 min. The method will enhance the quality of a tilt series by minimizing the amount of required focus‐change compensation by aligning the optical axis to the tilt axis of the specimen stage. The alignment is achieved by invoking an amount of image shift as deduced from the mathematical model describing the effect of specimen tilt. As examples for application in biological and materials sciences 3D reconstructions of a mitochondrion and a zeolite crystal are presented.     

An automatic method for identifying appropriate gradient magnitude for 3D boundary detection of confocal image stacks

Gradients play an important role in 2D image processing. Many edge detection algorithms are gradient‐based. We are interested in 3D boundary detection which can be considered as an extension of 2D edge detection in 3D space. In this paper, an algorithm to automatically and quantitatively measure the suitability of gradient magnitudes in detection of 3D boundary points of confocal image stacks is presented. A Measurement Function is defined to evaluate the suitability of each gradient magnitude chosen to be the threshold for 3D boundary detection. The application of Gauss's Divergence Theorem provides a solution to calculate the Measurement Function numerically. The gradient magnitude at which the maximum of the Measurement Function is achieved can be utilized as the most appropriate threshold for gradient‐based boundary detection and other operations like volume visualization.     

An automated cell viability quantification method for low‐resolution confocal images of closely packed cells based on a modified gradient flow tracking algorithm

Fluorescent‐based live/dead labelling combined with fluorescent microscopy is one of the widely used and reliable methods for assessment of cell viability. This method is, however, not quantitative. Many image‐processing methods have been proposed for cell quantification in an image. Among all these methods, several of them are capable of quantifying the number of cells in high‐resolution images with closely packed cells. However, no method has addressed the quantification of the number of cells in low‐resolution images containing closely packed cells with variable sizes. This paper presents a novel method for automatic quantification of live/dead cells in 2D fluorescent low‐resolution images containing closely packed cells with variable sizes using a mean shift‐based gradient flow tracking. Accuracy and performance of the method was tested on growth plate confocal images. Experimental results show that our algorithm has a better performance in comparison to other methods used in similar detection conditions.