Segmentation of whole cells and cell nuclei from 3-D optical microscope images using dynamic programming.

Communications between cells in large part drive tissue development and function, as well as disease-related processes such as tumorigenesis. Understanding the mechanistic bases of these processes necessitates quantifying specific molecules in adjacent cells or cell nuclei of intact tissue. However, a major restriction on such analyses is the lack of an efficient method that correctly segments each object (cell or nucleus) from 3-D images of an intact tissue specimen. We report a highly reliable and accurate semi-automatic algorithmic method for segmenting fluorescence-labeled cells or nuclei from 3-D tissue images. Segmentation begins with semi-automatic, 2-D object delineation in a user-selected plane, using dynamic programming (DP) to locate the border with an accumulated intensity per unit length greater that any other possible border around the same object. Then the two surfaces of the object in planes above and below the selected plane are found using an algorithm that combines DP and combinatorial searching. Following segmentation, any perceived errors can be interactively corrected. Segmentation accuracy is not significantly affected by intermittent labeling of object surfaces, diffuse surfaces, or spurious signals away from surfaces. The unique strength of the segmentation method was demonstrated on a variety of biological tissue samples where all cells, including irregularly shaped cells, were accurately segmented based on visual inspection.

A high-throughput system for segmenting nuclei using multiscale techniques.

Automatic segmentation of cell nuclei is critical in several high-throughput cytometry applications whereas manual segmentation is laborious and irreproducible. One such emerging application is measuring the spatial organization (radial and relative distances) of fluorescence in situ hybridization (FISH) DNA sequences, where recent investigations strongly suggest a correlation between nonrandom arrangement of genes to carcinogenesis. Current automatic segmentation methods have varying performance in the presence of nonuniform illumination and clustering, and boundary accuracy is seldom assessed, which makes them suboptimal for this application. The authors propose a modular and model-based algorithm for extracting individual nuclei. It uses multiscale edge reconstruction for contrast stretching and edge enhancement as well as a multiscale entropy-based thresholding for handling nonuniform intensity variations. Nuclei are initially oversegmented and then merged based on area followed by automatic multistage classification into single nuclei and clustered nuclei. Estimation of input parameters and training of the classifiers is automatic. The algorithm was tested on 4,181 lymphoblast nuclei with varying degree of background nonuniformity and clustering. It extracted 3,515 individual nuclei and identified single nuclei and individual nuclei in clusters with 99.8 +/- 0.3% and 95.5 +/- 5.1% accuracy, respectively. Segmented boundaries of the individual nuclei were accurate when compared with manual segmentation with an average RMS deviation of 0.26 microm (approximately 2 pixels). The proposed segmentation method is efficient, robust, and accurate for segmenting individual nuclei from fluorescence images containing clustered and isolated nuclei. The algorithm allows complete automation and facilitates reproducible and unbiased spatial analysis of DNA sequences.

Calibration and standardization of the emission light path of confocal microscopes.

Recently, there has been a large expansion in the usage of optical microscopes for obtaining quantitative information from biological samples in order to determine fundamental biological information such as molecular kinetics and interaction, and heterogeneity within cell populations. Consequently, we built a highly stable, uniform, isotropically emitting and convenient-to-use light source, and designed image analysis procedures for calibrating the emission light path of optical microscopes. We used the source and procedures to analyse the quantitative imaging properties of a widely used model of laser scanning confocal microscope. Results showed that the overall performance was as high as could be expected given the inherent limitations of the optical components and photomultiplier tubes. We observed that the photon detection efficiency did not vary with photomultiplier tube gain and that the highest dynamic range was achieved with relatively low gain and 12-bit digitization. Practical applications of the light source for checking the transmission of optical components in the emission light path are presented.

Segmentation of nuclei and cells using membrane related protein markers.

Segmenting individual cell nuclei from microscope images normally involves volume labelling of the nuclei with a DNA stain. However, this method often fails when the nuclei are tightly clustered in the tissue, because there is little evidence from the images on where the borders of the nuclei are. In this paper we present a method which solves this limitation and furthermore enables segmentation of whole cells. Instead of using volume stains, we used stains that specifically label the surface of nuclei or cells: lamins for the nuclear envelope and alpha-6 or beta-1 integrins for the cellular surface. The segmentation is performed by identifying unique seeds for each nucleus/cell and expanding the boundaries of the seeds until they reach the limits of the nucleus/cell, as delimited by the lamin or integrin staining, using gradient-curvature flow techniques. We tested the algorithm using computer-generated objects to evaluate its robustness against noise and applied it to cells in culture and to tissue specimens. In all the cases that we present the algorithm gave accurate results.

Modulation of lens cell adhesion molecules by particle beams.

Cell adhesion molecules (CAMs) are proteins which anchor cells to each other and to the extracellular matrix (ECM), but whose functions also include signal transduction, differentiation, and apoptosis. We are testing a hypothesis that particle radiations modulate CAM expression and this contributes to radiation-induced lens opacification. We observed dose-dependent changes in the expression of beta 1-integrin and ICAM-1 in exponentially-growing and confluent cells of a differentiating human lens epithelial cell model after exposure to particle beams. Human lens epithelial (HLE) cells, less than 10 passages after their initial culture from fetal tissue, were grown on bovine corneal endothelial cell-derived ECM in medium containing 15% fetal bovine serum and supplemented with 5 ng/ml basic fibroblast growth factor (FGF-2). Multiple cell populations at three different stages of differentiation were prepared for experiment: cells in exponential growth, and cells at 5 and 10 days post-confluence. The differentiation status of cells was characterized morphologically by digital image analysis, and biochemically by Western blotting using lens epithelial and fiber cell-specific markers. Cultures were irradiated with single doses (4, 8 or 12 Gy) of 55 MeV protons and, along with unirradiated control samples, were fixed using -20 degrees C methanol at 6 hours after exposure. Replicate experiments and similar experiments with helium ions are in progress. The intracellular localization of beta 1-integrin and ICAM-1 was detected by immunofluorescence using monoclonal antibodies specific for each CAM. Cells known to express each CAM were also processed as positive controls. Both exponentially-growing and confluent, differentiating cells demonstrated a dramatic proton-dose-dependent modulation (upregulation for exponential cells, downregulation for confluent cells) and a change in the intracellular distribution of the beta 1-integrin, compared to unirradiated controls. In contrast, there was a dose-dependent increase in ICAM-1 immunofluorescence in confluent, but not exponentially-growing cells. These results suggest that proton irradiation downregulates beta 1-integrin and upregulates ICAM-1, potentially contributing to cell death or to aberrant differentiation via modulation of anchorage and/or signal transduction functions. Quantification of the expression levels of the CAMs by Western analysis is in progress.

Three-dimensional image visualization and analysis.

This unit introduces the concepts of 3D image analysis and visualization as applied in cytometry. The author discusses the nature of 3D data sets and describes the techniques for visualization and analysis of 3D images. Discussions of noise removal, depth attenuation, and correction and segmentation are also included, as is a brief introduction to 3D analysis options and deconvolution principles. This commentary unit is a good way to begin an understanding of the application of 3D data sets.

Segmentation of confocal microscope images of cell nuclei in thick tissue sections.

Segmentation of intact cell nuclei from three-dimensional (3D) images of thick tissue sections is an important basic capability necessary for many biological research studies. However, segmentation is often difficult because of the tight clustering of nuclei in many specimen types. We present a 3D segmentation approach that combines the recognition capabilities of the human visual system with the efficiency of automatic image analysis algorithms. The approach first uses automatic algorithms to separate the 3D image into regions of fluorescence-stained nuclei and unstained background. This includes a novel step, based on the Hough transform and an automatic focusing algorithm to estimate the size of nuclei. Then, using an interactive display, each nuclear region is shown to the analyst, who classifies it as either an individual nucleus, a cluster of multiple nuclei, partial nucleus or debris. Next, automatic image analysis based on morphological reconstruction and the watershed algorithm divides clusters into smaller objects, which are reclassified by the analyst. Once no more clusters remain, the analyst indicates which partial nuclei should be joined to form complete nuclei. The approach was assessed by calculating the fraction of correctly segmented nuclei for a variety of tissue types: Caenorhabditis elegans embryos (839 correct out of a total of 848), normal human skin (343/362), benign human breast tissue (492/525), a human breast cancer cell line grown as a xenograft in mice (425/479) and invasive human breast carcinoma (260/335). Furthermore, due to the analyst's involvement in the segmentation process, it is always known which nuclei in a population are correctly segmented and which not, assuming that the analyst's visual judgement is correct.

Intensity-based energy transfer measurements in digital imaging microscopy.

Investigation of protein-protein associations is important in understanding structure and function relationships in living cells. Using Förster-type resonance energy transfer between donor and acceptor labeled monoclonal antibodies we can assess the cell surface topology of membrane proteins against which the antibodies were raised. In our current work we elaborated a quantitative image microscopic technique based on the measurement of fluorescence intensities to calculate the energy transfer efficiency on a pixel-by-pixel basis. We made use of the broad excitation and emission spectrum of cellular autofluorescence for background correction of images. In addition to the reference autofluorescence images (UV background) we recorded three fluorescent images (donor, acceptor and energy transfer signal) of donor-acceptor double labeled samples, and corrected for spectral spillage of the directly excited donor and acceptor fluorescence into the energy transfer image. After careful image registration we were able to calculate the energy transfer efficiency on a pixel-by -pixel basis. In this paper, we also present a critical comparison between results obtained with this method and other approaches (photobleaching and flow cytometric energy transfer measurements).

EGF-induced redistribution of erbB2 on breast tumor cells: flow and image cytometric energy transfer measurements.

erbB2, a member of the epidermal growth factor (EGF) receptor-type tyrosine kinase receptor family, is overexpressed in breast carcinomas with poor prognosis. We examined the cell surface association of this receptor with itself and with other cell surface proteins by the Förster-type fluorescence resonance energy transfer using whole antibodies and Fab fragments. We found that erbB2 molecules homoassociate in unstimulated SK-BR-3, BT474 and BT474-M (a metastatic version of the parent BT474 line) breast tumor cells, and that the interaction was enhanced by EGF treatment in suspensions of SK-BR-3 and BT474-M cells. BT474 cells (with low EGF receptor expression) and attached SK-BR-3 cells do not respond to EGF. Image microscopic energy transfer measurements found considerable pixel-by-pixel heterogeneity in the homoassociation state of erbB2. In accordance with the EGF-induced redistribution of erbB2, EGF receptor was found to be in close proximity to erbB2 in FRET measurements. By labeling different epitopes on erbB2 and the lipid bilayer, we were able to prepare an epitope map of erbB2 molecule. Our data suggest the existence of dynamic cell surface patterns of erbB2 and point to functions fulfilled by these molecular complexes.

Efficient, interactive, and three-dimensional segmentation of cell nuclei in thick tissue sections.

Segmentation of intact cell nuclei in three-dimensional (3D) images of thick tissue sections is an important basic capability necessary for many biological research studies. Because automatic algorithms do not correctly segment all nuclei in tissue sections, interactive algorithms may be preferable for some applications. Existing interactive segmentation algorithms require the analyst to draw a border around the nucleus under consideration in all successive two-dimensional (2D) planes of the 3D image. The present paper describes an algorithm with two main advantages over the existing method. First, the analyst draws borders only in 2D planes that cut approximately through the center of the nucleus under consideration so that the nuclear borders generally are most distinct. Second, the analyst draws only five borders around each nucleus, and then the algorithm interpolates the entire surface. The algorithm results in segmented objects that correspond to individual, visually identifiable nuclei. The segmented surfaces, however, may not exactly represent the true nuclear surface. An optional, automatic surface optimization algorithm can be applied to reduce this error.

Genotype/phenotype analyses of low frequency tumor cells using computerize image microscopy.

Techniques to identify low frequency (i.e., 10(-4)-10(-5)) tumor cells in bone marrow and peripheral blood of cancer patients provide opportunities for early detection of disseminated disease and characterization of the properties of cells released from the tumor. Low frequency epithelial cells in marrow and blood can be detected using immunophenotypic markers directed against intracellular and/or cell surface antigens. However, nonspecific phenotypic labeling may compromise the ability to discriminate tumor from nontumor cells. We describe optimization of slide-based approaches that facilitate identification and subsequent molecular cytogenetic characterization of rare tumor cell populations in hematopoietic tissues. Colon tumor cells seeded in a hematopoietic background provided a model system to optimize methodologies that are applicable to detection and quantification of micrometastases in clinical specimens. Mixtures of cytogenetically aberrant epithelial cells and hematopoietic cells on slides were labeled with an anti-cytokeratin 20 (anti-CK20) antibody that recognizes > 90% of colon adenocarcinomas. Computerized image analysis was used to record the location of the immunofluorescent cells on microscopic slides. Cells on slides were then hybridized using fluorescence in situ hybridization (FISH) with repeat-sequence DNA probes to detect aneusomies. Previously discriminated epithelial cells were relocated for molecular cytogenetic characterization. Tumor cells present at frequencies approximating 5 x 10(-5) were discriminated on the basis of immunofluorescence and cell size. A low frequency (4 x 10(-4)) population of normal hematopoietic cells also labeled with anti-CK20 (data not shown). This strategy of sequential immunophenotyping and molecular cytogenetic analyses may be useful to discriminate tumor from nontumor cells in cancer patients with micrometastatic disease.

Autofluorescence correction for fluorescence in situ hybridization.

Optimal sensitivity of fluorescence in situ hybridization (FISH) requires bright signals and low background fluorescence. Use of locus-specific probes is especially dependent on high sensitivity. Some tissue preparations show high autofluorescence, masking small or dim signals. We have developed a new method for subtracting autofluorescence from digital images on a pixel-by-pixel basis. It is based on the observation that fluorescent labels for FISH have narrower excitation and emission spectra than the chemical components responsible for autofluorescence. Our new approach uses calculation of the ratio of autofluorescence between multiple color images for correction of autofluorescence in each individual image. By subtracting autofluorescence components, we were able to enhance centromeric signals and make previously indistinguishable cosmid signals clearly visible. This image-processing approach to autofluorescence correction may widen the applicability of gene-specific probes in FISH analysis of tumor material.

Automatic detection of clustered, fluorescent-stained nuclei by digital image-based cytometry.

Automatic image-based cytometry (IC) can conveniently quantify the distributions of several specific, fluorescence-labeled molecules within individual, isolated cells of slide- or tissue-based specimens. However, many specimens contain clusters of cells or nuclei that are not detected as individual entities by existing automatic methods. We have developed analysis algorithms which detected individual nuclei occurring in clusters or as isolated nuclei. Specimens were labeled with a fluorescent DNA stain, imaged and the images were segmented into regions of nuclei and background. Clusters of nuclei, identified by their size and shape, were divided into individual nuclei by searching for dividing paths between nuclei. The paths, which need not be straight, possessed the highest average gradient per pixel. In addition, both high- and low-pass filtered images of the original image were analyzed. For each individual nucleus, one of the three segmented regions representing the nucleus (from either the original or one of two filtered images) was chosen as the final result, based on the closeness of the regions to average nuclear morphology. The algorithms correctly detected a high proportion of isolated (328/333) and clustered (254/271) nuclei when applied to images of 2 microns prostate and breast cancer sections. Thus, these algorithms should enable much more accurate detection and analyses of nuclei in intact specimens.

Detection of human papillomavirus type 16/18 DNA in cervicovaginal cells by fluorescence based in situ hybridization and automated image cytometry.

Automatic fluorescence image cytometry (AFIC) is a fast, sensitive, and reliable approach for screening slide-based clinical specimens. In this study, we applied AFIC to identify cancer-associated human papillomavirus (HPV) genotypes 16 and 18 in individual cells of cervical smears using a sensitive fluorescence based in situ hybridization (FISH) assay. HPV sequences were labeled by FISH and the cells imaged using an epi-fluorescence microscope coupled to a low-light color CCD camera. Before application to clinical specimens, AFIC was assessed using fluorescent calibration beads and cervical cancer cell lines containing known numbers of integrated HPV genomes per nucleus. Assessment showed that our AFIC had a linear response, was quantitatively accurate, and had the sensitivity to detect one HPV genome per nucleus. After acquisition of images, computer algorithms identified every cell nucleus (via a fluorescent DNA counterstain) and quantified the FISH signal per nucleus. AFIC was employed to screen 27 patient specimens for HPV 16/18, of which 12 were positive. The HPV status of the specimens positively correlated with the pathological diagnosis, and since AFIC automatically and correctly located every cell, it was possible to directly compare morphology and HPV status in the same cell. In conclusion, the combination of FISH and AFIC is a sensitive and quantitative method to detect high risk HPV sequences in cervical smears.

Co-localization of the tumor-suppressor protein p53 and human papillomavirus E6 protein in human cervical carcinoma cell lines.

The loss of the tumor-suppressor activity of p53, either by mutation or by interaction with the human papillomavirus (HPV) E6 protein, is considered to be an important mechanism in the carcinogenesis of cervical cancer. We have studied the cytological distribution of these proteins in human cervical carcinoma cell lines using polyclonal anti-p53 and monoclonal anti-E6 antibodies. The antibody specificity was confirmed by immunoblot and immunocompetition analyses. The intracellular localization of p53 and E6 was detected using the techniques of conventional and three-dimensional confocal microscopy. In the HPV-18 or -16 integrated cell lines, HeLa, CaSki and SiHa, viral oncoprotein E6 and endogenous tumor-suppressor protein, p53, were observed by immunofluorescence in the cytoplasm; p53 also had a weak punctate staining in the nuclei of HeLa and CaSki cells. In the HPV-negative cervical carcinoma cell lines, C-33A and HT-3, which have mutated p53, p53 was localized predominantly to the nucleus, with C-33A cells having elevated levels of p53 compared with the other cell lines. High spatial resolution imaging, using confocal microscopy, was performed on the cells after double fluorescence staining for p53 (fluorescein) and E6 (rhodamine). The images showed that both p53 and E6 had similar cytoplasmic distributions, which implied that these two proteins may exist as a cytoplasmic complex. To substantiate this implication, fluorescence resonance energy transfer microscopy was performed, which provided direct evidence of a close association between p53 and E6 within individual HeLa cells. The results from this study support the theory that p53 protein binds HPV-16/18 E6 protein in the cell cytoplasm, thus preventing p53 from exerting its tumor-suppressor function in the nucleus. Hence, inactivation of wild-type p53 by p53-E6 complex formation in cervical cancer may be a critical step in malignant transformation.

Quantitative precision of an automated, fluorescence-based image cytometer.

Digital image-based cytometry of clinical specimens labeled with fluorescent, disease-specific markers holds promise for becoming an important diagnostic and prognostic technique because the technique can make a diverse range of quantitative biochemical, morphologic, densitometric and contextual measurements on intact specimens. It has been previously shown by us, using an image cytometer (IC) consisting entirely of commercially available components, that the nuclei of individual cells in slide-supported specimens can be detected automatically using a fluorescent DNA stain and image analysis software. The purpose of this study was to determine the precision of the IC for quantifying the integrated fluorescence intensity and area of fluorescent standard beads and nuclei. Integrated intensities could be quantified to between 2.3% and 3.5% precision using a 40x objective lens and between 1.6% and 2.3% using a 20x objective. The main contribution to this uncertainty was 2% inaccuracy in determining the variations in sensitivity over the imaging area. Areas could be quantified to between 0.91% and 2.1% using a 40x objective and between 2.8% and 3.2% using a 20x objective. Significant quantification errors were introduced if the objects were not in focus or were touching each other. Overall, however, these results demonstrated that image cytometry of fluorescence-stained specimens can yield quantitative results with sufficient precision for determining DNA ploidy distributions and for making other measurements on clinical specimens.

Automated image-based cytometry with fluorescence-stained specimens.

The combination of digitized microscopy, algorithms for object recognition and fluorescent labeling is a promising approach for reliable, quick, automated and cost-effective screening of clinical specimens. We describe two conceptually different algorithms for detecting objects in fluorescence microscopic images. One, which is partially automated, compares a mask that represents a typical object with every position in the image; the other, which is fully automated, calculates threshold intensities to segment the image into regions of objects and background. Applications of the algorithms in conjunction with a prototype image-based cytometer are demonstrated for determining the DNA ploidy distribution of cultured human endometrial cells and determining the DNA ploidy distribution and the fraction of cells expressing the E6 antigen of human papilloma virus serotypes 16 and 18 in a PAP smear. The encouraging results from this study suggest that automated image-based cytometry utilizing fluorescent stains will be a valuable asset for clinical screening.

Automated fluorescence image cytometry. DNA quantification and detection of chlamydial infections.

Digitized fluorescence microscopy in conjunction with automated image segmentation is a promising approach for screening clinical specimens quickly and reliably. This paper describes the hardware and software of a prototype image-based cytometer that can identify fluorescent objects, discriminate true objects from artifacts and divide overlapping pairs of objects. The use of this image cytometer is discussed for: (1) the measurement of the DNA ploidy distribution of isolated mature rat liver nuclei labeled with 4',6-diamidine-2-phenylindole; (2) the comparison of the DNA ploidy distributions of the same samples measured by image cytometry (ICM) and flow cytometry (FCM); and (3) the quantification of chlamydial infection by double labeling cells with antichlamydiae antibody and Hoechst 33258 for nuclear DNA analysis. Ploidy distributions measured by the automated image cytometer compared favorably to those obtained by FCM. All pairs of overlapping nuclei were automatically detected by an additional computer algorithm, and those pairs that were clearly more than one nucleus by visual inspection were correctly divided. The irregular morphology of the chlamydiae-infected cells meant that 26% of them were not correctly identified in the fluorescein-stained images (as judged by manual inspection), but all cells were nevertheless detected correctly from the images of the Hoechst-stained samples. Automated fluorescence ICM yielded results similar to those obtained with FCM and had the additional benefit of maintaining cell and tissue architecture while preserving the opportunity for subsequent manual inspection of the specimen.

Is oleic acid the thyroxine binding inhibitor in the serum of ill patients?

The possibility that oleic acid is the thyroxine binding inhibitor in the serum of seriously ill patients was investigated. 3H-Oleic acid was shown to bind directly to human thyroxine-binding globulin (TBG) by the techniques of one and two-dimensional immunoelectrophoresis in combination with autoradiography. However, no correlation was seen between serum thyroxine concentration and oleic acid concentration in two groups of patients, one of which underwent routine cholecystectomy, whilst the other group was admitted to an intensive therapy unit (mortality 75%). No correlation was seen between serum total thyroxine concentration and either stearic, palmitic, linoleic or arachidonic acid concentrations in these groups. Therefore, it was concluded that oleic acid was unlikely to be the circulating inhibitor of thyroxine binding.

The enhancement of radiographic images from a multiwire camera using a maximum entropy algorithm.

The multiwire camera (MWC) produces high speed, quantitative autoradiography of radiolabelled substances in two-dimensional systems. While greatly superior to film-based systems in respect of speed and quantitativity the MWC has significantly poorer spatial resolution (particularly for high energy beta-emitting radiolabels) and the performance is ultimately limited by the noise induced in the images by Poisson statistics and counter background. Processing the MWC images with a maximum entropy algorithm significantly improves the performance of the system in these respects. The algorithm has been tested using one-dimensional data taken from images of known tritium, 14C and 125I distributions. Processed images are visually more acceptable with improved quantitative accuracy and spatial resolution. Quantitative accuracy, calculated as the root mean square deviation between an image and the known sample activities, is 10-40% lower for processed images compared with original camera images. Spatial resolution, calculated from slopes in the images representing edges of activity in the sources, is improved by 20-40% for the processed images. The algorithm is used to improve a two-dimensional image from a biological study. The source distribution consisted of a set of circular dots of varying activity. The dots with lowest activity were barely discernible in the raw MWC image but are clearly resolved after processing. The algorithm used is simple and effective and executes acceptably quickly on a personal computer. It should prove useful in any context where the imaging performance of a system is limited by Poisson statistics.