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.     

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.     

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.     

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.     

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.     

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。     

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.     

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.     

Change of chromatin morphology during the cell cycle detected by means of automated image analysis

Mouse embryo fibroblasts growing asynchronously in vitro stained with Feulgen method and their nuclear chromatin was analysed by means of the image analysing computer Quantimet 720D. Cells with 2C, 3C and 4C content of DNA were considered as being in G₁, middle S and G₂ phase of cell cycle, respectively. It was found that the projected area of nuclei increases during the cell cycle and that the mean optical density of chromatin increases from G₁ through S to G₂ phase. The curves showing the areas of chromatin at different optical density thresholds are different for cells in G₁, S and G₂ phase. The results demonstrate cyclic changes in chromatin morphology in the interphase nuclei during the cell cycle.     

Accurate, precise modeling of cell proliferation kinetics from time-lapse imaging and automated image analysis of agar yeast culture arrays

Background  

Genome-wide mutant strain collections have increased demand for high throughput cellular phenotyping (HTCP). For example, investigators use HTCP to investigate interactions between gene deletion mutations and additional chemical or genetic perturbations by assessing differences in cell proliferation among the collection of 5000 S. cerevisiae gene deletion strains. Such studies have thus far been predominantly qualitative, using agar cell arrays to subjectively score growth differences. Quantitative systems level analysis of gene interactions would be enabled by more precise HTCP methods, such as kinetic analysis of cell proliferation in liquid culture by optical density. However, requirements for processing liquid cultures make them relatively cumbersome and low throughput compared to agar. To improve HTCP performance and advance capabilities for quantifying interactions, YeastXtract software was developed for automated analysis of cell array images.     

Classification and measurement of fungal pellets by automated image analysis

An automated image analysis method for classifying and measuring pellets of filamentous fungi growing in submerged fermentations has been developed. The method discriminates between pelleted mycelial growth and loose aggregates of dispersed hyphae. Pellets are classified into smooth and hairy types. In both cases, the core of the pellet is identified and its shape and size characterized. For hairy pellets the annular region is also characterized. The method was tested on pellets of Aspergillus niger ATCC 11414 grown in a defined medium in shake flasks. This rapid method makes practical extensive studies on the morphology of pellets in submerged fermentations and the influence of fermentation conditions on that morphology.     

FogBank: a single cell segmentation across multiple cell lines and image modalities

Background

Many cell lines currently used in medical research, such as cancer cells or stem cells, grow in confluent sheets or colonies. The biology of individual cells provide valuable information, thus the separation of touching cells in these microscopy images is critical for counting, identification and measurement of individual cells. Over-segmentation of single cells continues to be a major problem for methods based on morphological watershed due to the high level of noise in microscopy cell images. There is a need for a new segmentation method that is robust over a wide variety of biological images and can accurately separate individual cells even in challenging datasets such as confluent sheets or colonies.

Results

We present a new automated segmentation method called FogBank that accurately separates cells when confluent and touching each other. This technique is successfully applied to phase contrast, bright field, fluorescence microscopy and binary images. The method is based on morphological watershed principles with two new features to improve accuracy and minimize over-segmentation.First, FogBank uses histogram binning to quantize pixel intensities which minimizes the image noise that causes over-segmentation. Second, FogBank uses a geodesic distance mask derived from raw images to detect the shapes of individual cells, in contrast to the more linear cell edges that other watershed-like algorithms produce.We evaluated the segmentation accuracy against manually segmented datasets using two metrics. FogBank achieved segmentation accuracy on the order of 0.75 (1 being a perfect match). We compared our method with other available segmentation techniques in term of achieved performance over the reference data sets. FogBank outperformed all related algorithms. The accuracy has also been visually verified on data sets with 14 cell lines across 3 imaging modalities leading to 876 segmentation evaluation images.

Conclusions

FogBank produces single cell segmentation from confluent cell sheets with high accuracy. It can be applied to microscopy images of multiple cell lines and a variety of imaging modalities. The code for the segmentation method is available as open-source and includes a Graphical User Interface for user friendly execution.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0431-x) contains supplementary material, which is available to authorized users.     

Dye free automated cell counting and analysis

We have developed an automated cell counting method that uses images obtained at multiple focal heights to enumerate cells in confluent culture. By taking the derivative of image intensity with respect to focal height using two complementary images, we are able to count high‐density monolayers of cells over a large image area. Our method resists errors arising from variability in the focal plane caused by flatness or tilt non‐uniformities with a minimal amount of focal plane alignment, allowing the automated collection of images across a large area. Biotechnol. Bioeng. © 2013 Wiley Periodicals, Inc.     

A statistically driven approach for image segmentation and signal extraction in cDNA microarrays.

The increasing use of cDNA microarrays necessitates the development of methods for extracting quality data. Here, we set forth hurdles to overcome in image analysis of microarrays. We emphasize the importance of objective data extraction methods resulting in reliable signal estimates. Based on statistical principles, we describe a method for automated grid alignment, spot detection, background estimation, flagging, and signal extraction. A software application that we call SignalViewer has been implemented for this method. We identify areas where we improved upon current methods used for array image analysis at each step in the process. Finally, we give examples to illustrate the performance of our algorithms on raw data.     

Sequential immunofluorescence staining and image analysis for detection of large numbers of antigens in individual cell nuclei.

BACKGROUND: Visualization of more than one antigen by multicolor immunostaining is often desirable or even necessary to explore spatial and temporal relationships of functional significance. Previously presented staining protocols have been limited to the visualization of three or four antigens. METHODS: Immunofluorescence staining was performed both on slices of formalin-fixed tissue and on cells in culture. Images of the stained material were recorded using digital imaging fluorescence microscopy. The primary and secondary antibodies, as well as the fluorophores, were thereafter removed using a combination of denaturation and elution techniques. After removal of the fluorescence stain, a new immunofluorescence staining was performed, visualizing a new set of antigens. The procedure was repeated up to three times. A method for image registration combined with segmentation, extraction of data, and cell classification was developed for efficient and objective analysis of the image data. RESULTS: The results show that immunofluorescence stains in many cases can be repeatedly removed without major effects on the antigenicity of the sample. CONCLUSIONS: The concentration of at least six different antigens in each cell can thus be measured semiquantitatively using sequential immunofluorescence staining and the described image analysis techniques. The number of antigens that can be visualized in a single sample is considerably increased by the presented protocol.