FISH Finder: a high-throughput tool for analyzing FISH images

MOTIVATION: Fluorescence in situ hybridization (FISH) is used to study the organization and the positioning of specific DNA sequences within the cell nucleus. Analyzing the data from FISH images is a tedious process that invokes an element of subjectivity. Automated FISH image analysis offers savings in time as well as gaining the benefit of objective data analysis. While several FISH image analysis software tools have been developed, they often use a threshold-based segmentation algorithm for nucleus segmentation. As fluorescence signal intensities can vary significantly from experiment to experiment, from cell to cell, and within a cell, threshold-based segmentation is inflexible and often insufficient for automatic image analysis, leading to additional manual segmentation and potential subjective bias. To overcome these problems, we developed a graphical software tool called FISH Finder to automatically analyze FISH images that vary significantly. By posing the nucleus segmentation as a classification problem, compound Bayesian classifier is employed so that contextual information is utilized, resulting in reliable classification and boundary extraction. This makes it possible to analyze FISH images efficiently and objectively without adjustment of input parameters. Additionally, FISH Finder was designed to analyze the distances between differentially stained FISH probes. AVAILABILITY: FISH Finder is a standalone MATLAB application and platform independent software. The program is freely available from: http://code.google.com/p/fishfinder/downloads/list.     

Adaptive Mesh Expansion Model (AMEM) for Liver Segmentation from CT Image

This study proposes a novel adaptive mesh expansion model (AMEM) for liver segmentation from computed tomography images. The virtual deformable simplex model (DSM) is introduced to represent the mesh, in which the motion of each vertex can be easily manipulated. The balloon, edge, and gradient forces are combined with the binary image to construct the external force of the deformable model, which can rapidly drive the DSM to approach the target liver boundaries. Moreover, tangential and normal forces are combined with the gradient image to control the internal force, such that the DSM degree of smoothness can be precisely controlled. The triangular facet of the DSM is adaptively decomposed into smaller triangular components, which can significantly improve the segmentation accuracy of the irregularly sharp corners of the liver. The proposed method is evaluated on the basis of different criteria applied to 10 clinical data sets. Experiments demonstrate that the proposed AMEM algorithm is effective and robust and thus outperforms six other up-to-date algorithms. Moreover, AMEM can achieve a mean overlap error of 6.8% and a mean volume difference of 2.7%, whereas the average symmetric surface distance and the root mean square symmetric surface distance can reach 1.3 mm and 2.7 mm, respectively.     

An approach for quantitative assessment of fluorescence in situ hybridization (FISH) signals for applied human molecular cytogenetics.

A number of applied molecular cytogenetic studies require the quantitative assessment of fluorescence in situ hybridization (FISH) signals (for example, interphase FISH analysis of aneuploidy by chromosome enumeration DNA probes; analysis of somatic pairing of homologous chromosomes in interphase nuclei; identification of chromosomal heteromorphism after FISH with satellite DNA probes for differentiation of parental origin of homologous chromosome, etc.). We have performed a pilot study to develop a simple technique for quantitative assessment of FISH signals by means of the digital capturing of microscopic images and the intensity measuring of hybridization signals using Scion Image software, commonly used for quantification of electrophoresis gels. We have tested this approach by quantitative analysis of FISH signals after application of chromosome-specific DNA probes for aneuploidy scoring in interphase nuclei in cells of different human tissues. This approach allowed us to exclude or confirm a low-level mosaic form of aneuploidy by quantification of FISH signals (for example, discrimination of pseudo-monosomy and artifact signals due to over-position of hybridization signals). Quantification of FISH signals was also used for analysis of somatic pairing of homologous chromosomes in nuclei of postmortem brain tissues after FISH with "classical" satellite DNA probes for chromosomes 1, 9, and 16. This approach has shown a relatively high efficiency for the quantitative registration of chromosomal heteromorphism due to variations of centromeric alphoid DNA in homologous parental chromosomes. We propose this approach to be efficient and to be considered as a useful tool in addition to visual FISH signal analysis for applied molecular cytogenetic studies.     

Bayesian estimation of probabilistic atlas for tissue segmentation

Automatic anatomical brain image segmentation is still a challenge. In particular, algorithms have to address the partial volume effect (PVE) as well as the variability of the gray level of internal brain structures which may appear closer to gray matter (GM) than white matter (WM). Atlas based segmentation is one solution as it brings prior information. For such tasks, probabilistic atlases are very useful as they take account of the PVE information. In this paper, we provide a detailed analysis of a generative statistical model based on dense deformable templates that represents several tissue types observed in medical images. The inputs are gray level data whereas our atlas is composed of both an estimation of the deformation metric and probability maps of each tissue (called class). This atlas is used to guide the tissue segmentation of new images. Experiments are shown on brain T1 MRI datasets. This method only requires approximate pre-registration, as the latter is done jointly with the segmentation. Note however that an approximate registration is a reasonable pre-requisite given the application.     

Segmentation and detection of fluorescent 3D spots

The 3D spatial organization of genes and other genetic elements within the nucleus is important for regulating gene expression. Understanding how this spatial organization is established and maintained throughout the life of a cell is key to elucidating the many layers of gene regulation. Quantitative methods for studying nuclear organization will lead to insights into the molecular mechanisms that maintain gene organization as well as serve as diagnostic tools for pathologies caused by loss of nuclear structure. However, biologists currently lack automated and high throughput methods for quantitative and qualitative global analysis of 3D gene organization. In this study, we use confocal microscopy and fluorescence in-situ hybridization (FISH) as a cytogenetic technique to detect and localize the presence of specific DNA sequences in 3D. FISH uses probes that bind to specific targeted locations on the chromosomes, appearing as fluorescent spots in 3D images obtained using fluorescence microscopy. In this article, we propose an automated algorithm for segmentation and detection of 3D FISH spots. The algorithm is divided into two stages: spot segmentation and spot detection. Spot segmentation consists of 3D anisotropic smoothing to reduce the effect of noise, top-hat filtering, and intensity thresholding, followed by 3D region-growing. Spot detection uses a Bayesian classifier with spot features such as volume, average intensity, texture, and contrast to detect and classify the segmented spots as either true or false spots. Quantitative assessment of the proposed algorithm demonstrates improved segmentation and detection accuracy compared to other techniques.     

Oufti: an integrated software package for high‐accuracy,high‐throughput quantitative microscopy analysis

With the realization that bacteria display phenotypic variability among cells and exhibit complex subcellular organization critical for cellular function and behavior, microscopy has re‐emerged as a primary tool in bacterial research during the last decade. However, the bottleneck in today's single‐cell studies is quantitative image analysis of cells and fluorescent signals. Here, we address current limitations through the development of Oufti, a stand‐alone, open‐source software package for automated measurements of microbial cells and fluorescence signals from microscopy images. Oufti provides computational solutions for tracking touching cells in confluent samples, handles various cell morphologies, offers algorithms for quantitative analysis of both diffraction and non‐diffraction‐limited fluorescence signals and is scalable for high‐throughput analysis of massive datasets, all with subpixel precision. All functionalities are integrated in a single package. The graphical user interface, which includes interactive modules for segmentation, image analysis and post‐processing analysis, makes the software broadly accessible to users irrespective of their computational skills.     

Whole cell segmentation in solid tissue sections.

BACKGROUND: Understanding the cellular and molecular basis of tissue development and function requires analysis of individual cells while in their tissue context. METHODS: We developed software to find the optimum border around each cell (segmentation) from two-dimensional microscopic images of intact tissue. Samples were labeled with a fluorescent cell surface marker so that cell borders were brighter than elsewhere. The optimum border around each cell was defined as the border with an average intensity per unit length greater that any other possible border around that cell, and was calculated using the gray-weighted distance transform. Algorithm initiation requiring the user to mark two points per cell, one approximately in the center and the other on the border, ensured virtually 100% correct segmentation. Thereafter segmentation was automatic. RESULTS: The method was highly robust, because intermittent labeling of the cell borders, diffuse borders, and spurious signals away from the border do not significantly affect the optimum path. Computer-generated cells with increasing levels of added noise showed that the approach was accurate provided the cell could be detected visually. CONCLUSIONS: We have developed a highly robust algorithm for segmenting images of surface-labeled cells, enabling accurate and quantitative analysis of individual cells in tissue.     

Automated image analysis for quantitative fluorescence in situ hybridization with environmental samples

When fluorescence in situ hybridization (FISH) analyses are performed with complex environmental samples, difficulties related to the presence of microbial cell aggregates and nonuniform background fluorescence are often encountered. The objective of this study was to develop a robust and automated quantitative FISH method for complex environmental samples, such as manure and soil. The method and duration of sample dispersion were optimized to reduce the interference of cell aggregates. An automated image analysis program that detects cells from 4',6'-diamidino-2-phenylindole (DAPI) micrographs and extracts the maximum and mean fluorescence intensities for each cell from corresponding FISH images was developed with the software Visilog. Intensity thresholds were not consistent even for duplicate analyses, so alternative ways of classifying signals were investigated. In the resulting method, the intensity data were divided into clusters using fuzzy c-means clustering, and the resulting clusters were classified as target (positive) or nontarget (negative). A manual quality control confirmed this classification. With this method, 50.4, 72.1, and 64.9% of the cells in two swine manure samples and one soil sample, respectively, were positive as determined with a 16S rRNA-targeted bacterial probe (S-D-Bact-0338-a-A-18). Manual counting resulted in corresponding values of 52.3, 70.6, and 61.5%, respectively. In two swine manure samples and one soil sample 21.6, 12.3, and 2.5% of the cells were positive with an archaeal probe (S-D-Arch-0915-a-A-20), respectively. Manual counting resulted in corresponding values of 22.4, 14.0, and 2.9%, respectively. This automated method should facilitate quantitative analysis of FISH images for a variety of complex environmental samples.     

Quantification of fluorescence in situ hybridization signals by image cytometry.

In this study we aimed at the development of a cytometric system for quantification of specific DNA sequences using fluorescence in situ hybridization (ISH) and digital imaging microscopy. The cytochemical and cytometric aspects of a quantitative ISH procedure were investigated, using human peripheral blood lymphocyte interphase nuclei and probes detecting high copy number target sequences as a model system. These chromosome-specific probes were labeled with biotin, digoxigenin, or fluorescein. The instrumentation requirements are evaluated. Quantification of the fluorescence ISH signals was performed using an epi-fluorescence microscope with a multi-wavelength illuminator, equipped with a cooled charge couple device (CCD) camera. The performance of the system was evaluated using fluorescing beads and a homogeneously fluorescing specimen. Specific image analysis programs were developed for the automated segmentation and analysis of the images provided by ISH. Non-uniform background fluorescence of the nuclei introduces problems in the image analysis segmentation procedures. Different procedures were tested. Up to 95% of the hybridization signals could be correctly segmented using digital filtering techniques (min-max filter) to estimate local background intensities. The choice of the objective lens used for the collection of images was found to be extremely important. High magnification objectives with high numerical aperture, which are frequently used for visualization of fluorescence, are not optimal, since they do not have a sufficient depth of field. The system described was used for quantification of ISH signals and allowed accurate measurement of fluorescence spot intensities, as well as of fluorescence ratios obtained with double-labeled probes.     

Hyperdiploidy and apparent aneusomy in mesothelial cells from non-malignant effusions as detected by fluorescence in situ hybridization (FISH)

Interphase cytogenetics by fluorescence in situ hybridization (FISH) can be used to detect malignant cells characterized by chromosomal aneuploidy. However, apparent aneusomy in normal "control" tissues has to be considered when using FISH as diagnostic tool. In effusions as model tissue exposed to metastasis, the definition of cut-off levels for background aneusomy by FISH was aimed in this study. Using centromeric probes representing chromosomes 7, 8, 11, 12, 17 and 18, extensive chromosome copy number enumeration by single-color FISH analysis was performed in pleural and ascitic effusions derived from 15 patients with various, non-malignant diseases. In all effusions, cells with gain of hybridization signals for several or all chromosomes tested were found (in up to 1.94% of cells). A consistent finding was high grade hyperdiploidy (>4 centromeric signals). Mesothelial elements mainly contributed to hyperdiploidy in effusions, as demonstrated by a combined analysis of FISH and immunocytochemistry with staining for cytokeratin. Dual-color FISH analysis showed that hyperdiploidy was predominantly corresponding to polyploidization; however, there were always minor cell populations classified as aneuploid by dual-color FISH. In conclusion, stringent criteria have to be applied to distinguish malignancy-related aneuploidy from background aneusomy by FISH.     

A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

This paper aims to make a review on the current segmentation algorithms used for medical images. Algorithms are classified according to their principal methodologies, namely the ones based on thresholds, the ones based on clustering techniques and the ones based on deformable models. The last type is focused on due to the intensive investigations into the deformable models that have been done in the last few decades. Typical algorithms of each type are discussed and the main ideas, application fields, advantages and disadvantages of each type are summarised. Experiments that apply these algorithms to segment the organs and tissues of the female pelvic cavity are presented to further illustrate their distinct characteristics. In the end, the main guidelines that should be considered for designing the segmentation algorithms of the pelvic cavity are proposed.     

A multi-model approach to simultaneous segmentation and classification of heterogeneous populations of cell nuclei in 3D confocal microscope images.

Automated segmentation and morphometry of fluorescently labeled cell nuclei in batches of 3D confocal stacks is essential for quantitative studies. Model-based segmentation algorithms are attractive due to their robustness. Previous methods incorporated a single nuclear model. This is a limitation for tissues containing multiple cell types with different nuclear features. Improved segmentation for such tissues requires algorithms that permit multiple models to be used simultaneously. This requires a tight integration of classification and segmentation algorithms. Two or more nuclear models are constructed semiautomatically from user-provided training examples. Starting with an initial over-segmentation produced by a gradient-weighted watershed algorithm, a hierarchical fragment merging tree rooted at each object is built. Linear discriminant analysis is used to classify each candidate using multiple object models. On the basis of the selected class, a Bayesian score is computed. Fragment merging decisions are made by comparing the score with that of other candidates, and the scores of constituent fragments of each candidate. The overall segmentation accuracy was 93.7% and classification accuracy was 93.5%, respectively, on a diverse collection of images drawn from five different regions of the rat brain. The multi-model method was found to achieve high accuracy on nuclear segmentation and classification by correctly resolving ambiguities in clustered regions containing heterogeneous cell populations.     

Laser scanning confocal microscopy and quantitative microscopy with a charge coupled device camera improve detection of human papillomavirus DNA revealed by fluorescence in situ hybridization

Epithelial cervical CaSki, SiHa and HeLa cells containing respectively 600 copies of human papillomavirus (HPV) DNA type 16, 1–2 copies of HPV DNA type 16 and 10–50 copies of HPV DNA type 18 were used as model to detect different quantities of integrated HPV genome. The HPV DNA was identified on cell deposits with specific biotinylated DNA probes either by enzymatic in situ hybridization (EISH) or fluorescence in situ hybridization (FISH) involving successively a rabbit anti-biotin antibody, a biotinylated goat anti-rabbit antibody and streptavidin-alkaline phosphatase complex or streptavidin-fluorescein isothiocyanate complex. With brightfield microscopy and EISH, hybridization spots were observed in CaSki and HeLa cells but hardly any in SiHa cells. With fluorescence microscopy and FISH, hybridization spots were clearly seen only on CaSki cell nuclei. In an attempt to improve the detection of low quantities of HPV DNA signals revealed by FISH, laser scanning confocal microscopy (LSCM) and quantitative microscopy with an intensified charge coupled device (CCD) camera were used. With both LSCM and quantitative microscopy, as few as 1–2 copies of HPV DNA were detected and found to be confined to cell nuclei counterstained with propidium iodide. Under Nomarski phase contrast, a good preservation of the cell structure was observed. With quantitative microscopy, differences in the number, size, total area and integrated fluorescence intensity of hybridization spots per nucleus were revealed between CaSki, SiHa and HeLa cells. Considered altogether our results shows that in situ hybridization is a powerful technique to detect small amounts of nucleic acid sequences but the choice of the technique for cell examination is important. Single genes of HPV were visualized most efficiently by association of FISH with LSCM or quantitative microscopy with an intensified CCD camera.     

Methods for acquisition of quantitative from confocal images of gene expression in situ

In this review we summarize original methods for the extraction quantitative information from the confocal images of gene expression patterns. These methods include image segmentation, extraction of quantitative numerical data on gene expression, removal of background signal and spatial registration. Finally it is possible to construct a spatiotemporal atlas of gene expression form individual images obtained at each developmental stage. Initially all methods were developed to extract quantitative numerical information form confocal images of segmentation gene expression in Drosophila melanogaster. Application of these methods to Drosophila images makes it possible to reveal new mechanisms of formation of segmentation gene expression domains, as well as to construct the quantitative atlas of segmentation gene expression. Most image processing procedures can be easily adapted to process a wide range of biological images.     

Automatic telomere length measurements in interphase nuclei by IQ-FISH.

To benefit from the fluorescence-based automatic microscope (FLAME), we have adapted a PNA FISH technique to automatically determine telomere length in interphase nuclei. The method relies on the simultaneous acquisition of pan-telomeric signals and reference probe signals. We compared the quantitative figures to those for existing methods, i.e. Southern blot analysis and quantitative FISH (Q-FISH). Quantitative-FISH on interphase nuclei (IQ-FISH) allows the exact quantification of telomere length in interphase nuclei. Thus, this enables us to obtain not only exact information on the telomere length, but also morphological and topological details. The automatic measurement of large cell numbers allows the measurement of statistically relevant cell populations.     

Automated force volume image processing for biological samples

Atomic force microscopy (AFM) has now become a powerful technique for investigating on a molecular level, surface forces, nanomechanical properties of deformable particles, biomolecular interactions, kinetics, and dynamic processes. This paper specifically focuses on the analysis of AFM force curves collected on biological systems, in particular, bacteria. The goal is to provide fully automated tools to achieve theoretical interpretation of force curves on the basis of adequate, available physical models. In this respect, we propose two algorithms, one for the processing of approach force curves and another for the quantitative analysis of retraction force curves. In the former, electrostatic interactions prior to contact between AFM probe and bacterium are accounted for and mechanical interactions operating after contact are described in terms of Hertz-Hooke formalism. Retraction force curves are analyzed on the basis of the Freely Jointed Chain model. For both algorithms, the quantitative reconstruction of force curves is based on the robust detection of critical points (jumps, changes of slope or changes of curvature) which mark the transitions between the various relevant interactions taking place between the AFM tip and the studied sample during approach and retraction. Once the key regions of separation distance and indentation are detected, the physical parameters describing the relevant interactions operating in these regions are extracted making use of regression procedure for fitting experiments to theory. The flexibility, accuracy and strength of the algorithms are illustrated with the processing of two force-volume images, which collect a large set of approach and retraction curves measured on a single biological surface. For each force-volume image, several maps are generated, representing the spatial distribution of the searched physical parameters as estimated for each pixel of the force-volume image.     

Three-dimensional segmentation of nuclei and mitotic chromosomes for the study of cell divisions in live Drosophila embryos

Drosophila embryogenesis is an established model to investigate mechanisms and genes related to cell divisions in an intact multicellular organism. Progression through the cell cycle phases can be monitored in vivo using fluorescently labeled fusion proteins and time-lapse microscopy. To measure cellular properties in microscopic images, accurate and fast image segmentation methods are a critical prerequisite. To quantify static and dynamic features of interphase nuclei and mitotic chromosomes, we developed a three-dimensional (3D) segmentation method based on multiple level sets. We tested our method on 3D time-series images of live embryos expressing histone-2Av-green fluorescence protein. Our method is robust to low signal-to-noise ratios inherent to high-speed imaging, fluorescent signals in the cytoplasm, and dynamic changes of shape and texture. Comparisons with manual ground-truth segmentations showed that our method achieves more than 90% accuracy on the object as well as voxel levels and performs consistently throughout all cell cycle phases and developmental stages from syncytial blastoderm to postblastoderm mitotic domains.     

Semi-automated mitral valve morphometry and computational stress analysis using 3D ultrasound

In vivo human mitral valves (MV) were imaged using real-time 3D transesophageal echocardiography (rt-3DTEE), and volumetric images of the MV at mid-systole were analyzed by user-initialized segmentation and 3D deformable modeling with continuous medial representation, a compact representation of shape. The resulting MV models were loaded with physiologic pressures using finite element analysis (FEA). We present the regional leaflet stress distributions predicted in normal and diseased (regurgitant) MVs. Rt-3DTEE, semi-automated leaflet segmentation, 3D deformable modeling, and FEA modeling of the in vivo human MV is tenable and useful for evaluation of MV pathology.     

Methods for acquisition of quantitative data from confocal images of gene expression in situ

In this review, we summarize original methods for the extraction of quantitative information from confocal images of gene-expression patterns. These methods include image segmentation, the extraction of quantitative numerical data on gene expression, and the removal of background signal and spatial registration. Finally, it is possible to construct a spatiotemporal atlas of gene expression from individual images recorded at each developmental stage. Initially all methods were developed to extract quantitative numerical information from confocal images of segmentation gene expression in Drosophila melanogaster. The application of these methods to Drosophila images makes it possible to reveal new mechanisms in the formation of segmentation gene expression domains, as well as to construct a quantitative atlas of segmentation gene expression. Most image processing procedures can be easily adapted to process a wide range of biological images.     

Scanning Near-field Optical/Atomic Force Microscopy detection of fluorescence in situ hybridization signals beyond the optical limit

Fluorescence in situ hybridization (FISH) is widely used in molecular biological study. However, high-resolution analysis of fluorescent signals is theoretically limited by the 300-nm resolution optical limit of light microscopy. As an alternative to detection by light microscopy, we used Scanning Near-field Optical/Atomic Force Microscopy (SNOM/AFM), which can simultaneously obtain topographic and fluorescent images with nanometer-scale resolution. In this study, we demonstrated high-resolution SNOM/AFM imaging of barley chromosome (Hordeum vulgare, cv. Minorimugi) FISH signals using telomeric DNA probes. Besides detecting the granular structures on chromosomes in a topographic image, we clearly detected fluorescent signals in telomeric regions with low-magnification imaging. The high-resolution analysis suggested that one of the telomeric signals could be observed by expanded imaging as two fluorescent regions separated by approximately 250 nm. This result indicated that the fluorescent signals beyond the optical limit were detected with higher resolution scanning by SNOM/AFM.