Topology-based fluorescence image analysis for automated cell identification and segmentation

Cell segmentation refers to the body of techniques used to identify cells in images and extract biologically relevant information from them; however, manual segmentation is laborious and subjective. We present Topological Boundary Line Estimation using Recurrence Of Neighbouring Emissions (TOBLERONE), a topological image analysis tool which identifies persistent homological image features as opposed to the geometric analysis commonly employed. We demonstrate that topological data analysis can provide accurate segmentation of arbitrarily-shaped cells, offering a means for automatic and objective data extraction. One cellular feature of particular interest in biology is the plasma membrane, which has been shown to present varying degrees of lipid packing, or membrane order, depending on the function and morphology of the cell type. With the use of environmentally-sensitive dyes, images derived from confocal microscopy can be used to quantify the degree of membrane order. We demonstrate that TOBLERONE is capable of automating this task.     

Detailed interrogation of trypanosome cell biology via differential organelle staining and automated image analysis

Background  

Many trypanosomatid protozoa are important human or animal pathogens. The well defined morphology and precisely choreographed division of trypanosomatid cells makes morphological analysis a powerful tool for analyzing the effect of mutations, chemical insults and changes between lifecycle stages. High-throughput image analysis of micrographs has the potential to accelerate collection of quantitative morphological data. Trypanosomatid cells have two large DNA-containing organelles, the kinetoplast (mitochondrial DNA) and nucleus, which provide useful markers for morphometric analysis; however they need to be accurately identified and often lie in close proximity. This presents a technical challenge. Accurate identification and quantitation of the DNA content of these organelles is a central requirement of any automated analysis method.     

Visualization and correction of automated segmentation,tracking and lineaging from 5-D stem cell image sequences

Background

Neural stem cells are motile and proliferative cells that undergo mitosis, dividing to produce daughter cells and ultimately generating differentiated neurons and glia. Understanding the mechanisms controlling neural stem cell proliferation and differentiation will play a key role in the emerging fields of regenerative medicine and cancer therapeutics. Stem cell studies in vitro from 2-D image data are well established. Visualizing and analyzing large three dimensional images of intact tissue is a challenging task. It becomes more difficult as the dimensionality of the image data increases to include time and additional fluorescence channels. There is a pressing need for 5-D image analysis and visualization tools to study cellular dynamics in the intact niche and to quantify the role that environmental factors play in determining cell fate.

Results

We present an application that integrates visualization and quantitative analysis of 5-D (x,y,z,t,channel) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. Our application automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks.

Conclusions

By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. We combine unsupervised image analysis algorithms with an interactive visualization of the results. Our validation interface allows for each data set to be corrected to 100% accuracy, ensuring that downstream data analysis is accurate and verifiable. Our tool is the first to combine all of these aspects, leveraging the synergies obtained by utilizing validation information from stereo visualization to improve the low level image processing tasks.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-328) contains supplementary material, which is available to authorized users.     

New technologies for automated cell counting based on optical image analysis `The Cellscreen'

A prototype of a newly developed apparatus for measuring cell growth characteristics of suspension cells in micro titre plates over a period of time was examined. Fully automated non-invasive cell counts in small volume cultivation vessels, e.g. 96 well plates, were performed with the Cellscreen system by Innovatis AG, Germany. The system automatically generates microscopic images of suspension cells which had sedimented on the base of the well plate. The total cell number and cell geometry was analysed without staining or sampling using the Cedex image recognition technology. Thus, time course studies of cell growth with the identical culture became possible. Basic parameters like the measurement range, the minimum number of images which were required for statistically reliable results, as well as the influence of the measurement itself and the effect of evaporation in 96 well plates on cell proliferation were determined. A comparison with standard methods including the influence of the cultured volume per well (25 l to 200 l) on cell growth was performed. Furthermore, the toxic substances ammonia, lactate and butyrate were used to show that the Cellscreen system is able to detect even the slightest changes in the specific growth rate.     

Objective identification of cell cycle phases and subphases by automated image analysis.

Frequency distributions of integrated optical density, perimeter, projection, area, form factor, average optical density, and mean dispersion path of nuclear images of Feulgen-stained HeLa S3 cells were obtained by automated image analysis at the base threshold of 0.04 OD. The mean values and standard deviations of these geometric parameters were then computed versus increasing values of threshold (0.08--0.32 OD). There is clear evidence of differential chromatin dispersion and convolution during the cycle of synchronized HeLa S3 cells at different times after selective mitotic detachment. The combination of average OD, form factor, and mean dispersion path at base threshold with the threshold dependence of nuclear morphometric parameters permits objective identification of cell cycle phases and their subphases, by characterizing variations in chromatin geometry within and between phases, regardless of whether DNA content remains constant (early G1, middle G1, late G1), varies only slightly (late G1-early S or late S-G2 transitions), or varies significantly (early S-middle S).     

Contextual analysis and intermediate cell markers enhance high-resolution cell image analysis for automated cervical smear diagnosis.

Until now, efforts to automate cervical smear diagnosis have focused on analyzing features of individual cells. In a complex specimen such as that obtained from a cervical scrape, diagnostically significant cells may not be adequately represented or may elude detection by the automated technology. An approach is needed that extracts additional quantitative information from cervical smears beyond what the cell-by-cell approach can provide. A new methodology, contextual analysis, was developed to extract global quantitative information about cells, cell clusters, and background debris. This pilot study was designed to compare the efficacy of contextual analysis with high-resolution, single cell analysis and the analysis of intermediate cell markers. Thirty-four samples prepared as monolayers and stained with the Feulgen-Thionin/Congo Red stain were measured. Contextual analysis alone was able to classify 91% of the smears correctly; single cell analysis classified 94% of the cells correctly; and the intermediate cell analysis correctly identified the smear diagnosis for 84% of the cells. When all three analysis methods were combined into a simple smear level classifier, the overall smear classification accuracy was improved over those obtained using the three methodologies alone.     

Influence of optical density on the automated segmentation of rat kidney lysosomes.

After staining for acid phosphatase, video-images were acquired from 0.5-micron sections of rat kidney. Lysosomes in proximal tubules were automatically segmented, using a VICOM digital image processor and measured for area, number and optical density (OD). The purpose of this study is to objectively evaluate the performance of the automated segmentation algorithm at different staining intensities (a) by measuring area after staining with different incubation times, reduced substrate concentration or by adding an inhibitor and (b) by 'simulating' a decrease in OD (reducing grey-values at each point of a digitized image). The results of the experiments showed that: (1) the algorithm will underestimate the size of lysosomes (a) when the OD in close to the local background and (b) when an area is larger than or close to the area of the lowpass square filter; (2) accuracy of the segmentation can be improved by comparing the results of feature extraction after segmentation of the same image at different relative OD levels; (3) lysosomes with very low OD, compared to background are delineated with a large error or not delineated at all and this cannot be corrected. Incorrectly delineated lysosomes can be identified and excluded from further calculations, or their measured area replaced by an estimate of the true area.     

Improved staining boxes for fast, uniform staining of ultrathin sections on grids.

A standard LKB (LKB-Produkter Ab. S-161, 25 Brommma 1, Sweden) grid storage box is converted into several grid staining boxes by sawing the body of the box into segments along rows of its grid storage cavities. The staining boxes can be cut out to any required size or shape. The polymethyacrylate storage box cover is discarded. Covers for the staining boxes are cut from thin sheet vinyl, which is more chemically resistant than polymethyacrylate. Corresponding 2 mm diameter holes are drilled through the vinyl covers and the bottoms of the grid storage cavities of the staining boxes to convert the storage cavities into staining chambers. For staining, the covers are tied to the boxes with sewing thread and the assembled units are put into vials. The separate staining chambers prevent intermingling of and mechanical damage to grids during the staining procedure. Ultrathin sections are more cleanly and uniformly stained in bulk by the use of these staining boxes than they are when stained individually by a standard method.     

Fully automated segmentation and morphometrical analysis of muscle fiber images.

BACKGROUND: Measurement of muscle fiber size and determination of size distribution is important in the assessment of neuromuscular disease. Fiber size estimation by simple inspection is inaccurate and subjective. Manual segmentation and measurement are time-consuming and tedious. We therefore propose an automated image analysis method for objective, reproducible, and time-saving measurement of muscle fibers in routinely hematoxylin-eosin stained cryostat sections. METHODS: The proposed segmentation technique makes use of recent advances in level set based segmentation, where classical edge based active contours are extended by region based cues, such as color and texture. Segmentation and measurement are performed fully automatically. Multiple morphometric parameters, i.e., cross sectional area, lesser diameter, and perimeter are assessed in a single pass. The performance of the computed method was compared to results obtained by manual measurement by experts. RESULTS: The correct classification rate of the computed method was high (98%). Segmentation and measurement results obtained manually or automatically did not reveal any significant differences. CONCLUSIONS: The presented region based active contour approach has been proven to accurately segment and measure muscle fibers. Complete automation minimizes user interaction, thus, batch processing, as well as objective and reproducible muscle fiber morphometry are provided.     

Comparison of Papanicolaou staining and Feulgen staining for automated prescreening

Feulgen staining is considered to be a quantitative DNA-specific cytochemical procedure. The applicability of this staining in high-resolution cytometry was tested in comparison with a regressive Papanicolaou staining. Papanicolaou-stained or Feulgen-stained intermediate and carcinoma cells selected by a cytologist were examined with a Zeiss scanning microscope photometer at 546 and 560 nm, respectively. After cell image segmentation and feature extraction, a statistical data evaluation was carried out by computer. Cell distributions with respect to four selected nuclear features demonstrated the influence of the staining procedure on cell feature measurements. The discriminatory power of the classification system as related to both staining procedures was studied using discriminant analysis. Using only nuclear features, a 7.3% improvement of the overall correct classification rate (from 85.0% to 92.3%) was achieved using Feulgen staining. The misclassification rate was simultaneously reduced by 50%. Using cytoplasmic as well as nuclear features, a 98% rate of correct classification was achieved.     

Computed tomography and automated image analysis of prehistoric femora

Non-invasive characterization of limb bone cross-sectional geometry would be useful for biomechanical analyses of skeletal collections. Computed tomography (CT) is potentially the method of choice. Additionally, CT images are suitable for automated analysis. CT is here shown to be both accurate and precise in the analysis of cross-sectional geometry of prehistoric femora. Beam hardening artifacts can be reduced by using a water bath. As the availability of CT for research increases, both bone density and geometry could be determined simultaneously with this method.     

Analysis of fast dynamic processes in living cells: high-resolution and high-speed dual-color imaging combined with automated image analysis

The generation of spectral mutants of the green fluorescent protein (GFP) set the stage for multiple-color imaging in living cells. However, the use of this technique has been limited by a spectral overlap of the available GFP mutants and/or by insufficient resolution in both time and space. Using a new setup for dual-color imaging, we demonstrate here the visualization of small, fast moving vesicular structures with a high time resolution. Two GFP-fusion proteins were generated: human chromogranin B, a secretory granule matrix protein, and phogrin, a secretory granule membrane protein. They were tagged with enhanced yellow fluorescent protein (EYFP) and enhanced cyan fluorescent protein (ECFP), respectively. Both fusion proteins were cotransfected in Vero cells, a cell line from green monkey kidney. EYFP and ECFP were excited sequentially at high time rates using a monochromator. Charged coupled device (CCD)-based image acquisition resulted in 5-8 dual-color images per second, with a resolution sufficient to detect transport vesicles in mammalian cells. Under these conditions, a fully automated time-resolved analysis of the movement of color-coded objects was achieved. The development of specialized software permitted the analysis of the extent of colocalization between the two differentially labeled sets of cellular structures over time. This technical advance will provide an important tool to study the dynamic interactions of subcellular structures in living cells.     

A fast global fitting algorithm for fluorescence lifetime imaging microscopy based on image segmentation

Global fitting algorithms have been shown to improve effectively the accuracy and precision of the analysis of fluorescence lifetime imaging microscopy data. Global analysis performs better than unconstrained data fitting when prior information exists, such as the spatial invariance of the lifetimes of individual fluorescent species. The highly coupled nature of global analysis often results in a significantly slower convergence of the data fitting algorithm as compared with unconstrained analysis. Convergence speed can be greatly accelerated by providing appropriate initial guesses. Realizing that the image morphology often correlates with fluorophore distribution, a global fitting algorithm has been developed to assign initial guesses throughout an image based on a segmentation analysis. This algorithm was tested on both simulated data sets and time-domain lifetime measurements. We have successfully measured fluorophore distribution in fibroblasts stained with Hoechst and calcein. This method further allows second harmonic generation from collagen and elastin autofluorescence to be differentiated in fluorescence lifetime imaging microscopy images of ex vivo human skin. On our experimental measurement, this algorithm increased convergence speed by over two orders of magnitude and achieved significantly better fits.     

SuperSegger: robust image segmentation,analysis and lineage tracking of bacterial cells

Many quantitative cell biology questions require fast yet reliable automated image segmentation to identify and link cells from frame‐to‐frame, and characterize the cell morphology and fluorescence. We present SuperSegger, an automated MATLAB‐based image processing package well‐suited to quantitative analysis of high‐throughput live‐cell fluorescence microscopy of bacterial cells. SuperSegger incorporates machine‐learning algorithms to optimize cellular boundaries and automated error resolution to reliably link cells from frame‐to‐frame. Unlike existing packages, it can reliably segment microcolonies with many cells, facilitating the analysis of cell‐cycle dynamics in bacteria as well as cell‐contact mediated phenomena. This package has a range of built‐in capabilities for characterizing bacterial cells, including the identification of cell division events, mother, daughter and neighbouring cells, and computing statistics on cellular fluorescence, the location and intensity of fluorescent foci. SuperSegger provides a variety of postprocessing data visualization tools for single cell and population level analysis, such as histograms, kymographs, frame mosaics, movies and consensus images. Finally, we demonstrate the power of the package by analyzing lag phase growth with single cell resolution.     

Validation of image analysis for enzyme histochemical and immunocytochemical staining

Immunocytochemical and enzyme histochemical analyses of cells and tissues are used to detect changes in the extent of injury and the expression of various molecules. Image analysis quantitation offers an easier, more efficient technique to evaluate these changes. We studied the application of image analysis for evaluating enzyme histochemistry and immunocytochemistry of cells and tissues as a way to assess stroke. Using brain sections, we compared investigator and computer-generated image analysis of 2,3,5-triphenyltetrazolium chloride stained cerebral infarcts in rats subjected to 2 h middle cerebral artery occlusion and 22 h re-perfusion. Both methods documented the infarct volumes with a comparison of means of less then 5%. This suggests no difference between computer- and hand-calculated values. Computer-generated analysis was easier and faster to use. Using endothelial cell monolayers, immunocytochemical staining of a time course of heat shock protein expression was compared to a grading system using fast red chromagen counterstained with hematoxylin. Results demonstrated greater ease and efficiency with computer-generated image analysis compared to other subjective systems of analysis. Image analysis is more useful for detecting small differences in staining, especially when using 3,3-diaminobenzidine as a chromagen. Investigator bias is also reduced using this system. Our comparisons validate the use of this versatile technology to assess more easily both cell and tissues in stroke research.     

Validation of image analysis for enzyme histochemical and immunocytochemical staining

Immunocytochemical and enzyme histochemical analyses of cells and tissues are used to detect changes in the extent of injury and the expression of various molecules. Image analysis quantitation offers an easier, more efficient technique to evaluate these changes. We studied the application of image analysis for evaluating enzyme histochemistry and immunocytochemistry of cells and tissues as a way to assess stroke. Using brain sections, we compared investigator and computer-generated image analysis of 2,3,5-triphenyltetrazolium chloride stained cerebral infarcts in rats subjected to 2 h middle cerebral artery occlusion and 22 h re-perfusion. Both methods documented the infarct volumes with a comparison of means of less then 5%. This suggests no difference between computer- and hand-calculated values. Computer-generated analysis was easier and faster to use. Using endothelial cell monolayers, immunocytochemical staining of a time course of heat shock protein expression was compared to a grading system using fast red chromagen counterstained with hematoxylin. Results demonstrated greater ease and efficiency with computer-generated image analysis compared to other subjective systems of analysis. Image analysis is more useful for detecting small differences in staining, especially when using 3,3-diaminobenzidine as a chromagen. Investigator bias is also reduced using this system. Our comparisons validate the use of this versatile technology to assess more easily both cell and tissues in stroke research.     

Novel cell segmentation and online SVM for cell cycle phase identification in automated microscopy

MOTIVATION: Automated identification of cell cycle phases captured via fluorescent microscopy is very important for understanding cell cycle and for drug discovery. In this article, we propose a novel cell detection method that utilizes both the intensity and shape information of the cell for better segmentation quality. In contrast to conventional off-line learning algorithms, an Online Support Vector Classifier (OSVC) is thus proposed, which removes support vectors from the old model and assigns new training examples weighted according to their importance to accommodate the ever-changing experimental conditions. RESULTS: We image three cell lines using fluorescent microscopy under different experiment conditions, including one treated with taxol. Then, we segment and classify the cell types into interphase, prophase, metaphase and anaphase. Experimental results show the effectiveness of the proposed system in image segmentation and cell phase identification. AVAILABILITY: The software and test datasets are available from the authors.     

基于图像处理的血液细胞特征提取

利用数学形态学知识和图像处理方法,对缺铁性贫血的血液显微图像进行了分析,编制了相应的计算程序,对选取的区域内细胞的个数、半径和面积等重要参数进行了统计和处理,这对进一步研究细胞及其组织变化、医学临床诊断等问题,具有一定的指导意义。     

CellProfiler: free, versatile software for automated biological image analysis

Careful visual examination of biological samples is quite powerful, but many visual analysis tasks done in the laboratory are repetitive, tedious, and subjective. Here we describe the use of the open-source software, CellProfiler, to automatically identify and measure a variety of biological objects in images. The applications demonstrated here include yeast colony counting and classifying, cell microarray annotation, yeast patch assays, mouse tumor quantification, wound healing assays, and tissue topology measurement. The software automatically identifies objects in digital images, counts them, and records a full spectrum of measurements for each object, including location within the image, size, shape, color intensity, degree of correlation between colors, texture (smoothness), and number of neighbors. Small numbers of images can be processed automatically on a personal computer and hundreds of thousands can be analyzed using a computing cluster. This free, easy-to-use software enables biologists to comprehensively and quantitatively address many questions that previously would have required custom programming, thereby facilitating discovery in a variety of biological fields of study.