KEYWORDS: Image segmentation, In vitro testing, Tumors, Image filtering, Phase contrast, Image analysis, Linear filtering, Microscopy, Cancer, In vivo imaging
Early stages of tumor angiogenesis can be modeled by various in vitro cultures in which endothelial cells
(ECs) form networks that are considered to mimic the vascularization of tumors in vivo. Image based
quantification of EC culture model is a useful method for effective characterization of early stage in vitro
vasculogenesis and the effects of pro and anti-angiogenesis reagents. We propose an image analysis method
to quantify the EC tube formation in 2D cultures. The method segments images by high pass filtering in
Fourier space, followed by thresholding and a skeletonization and pruning process to generate the binary
skeleton image of the cell patterns in culture. Several quantities such as the network entropy (NE), the node
number, total number of chords, total and average chord length were used to quantify the evolution of EC
tubes. The automatic measurement of chord length was validated against manual measurement, achieving
an R2 value of 0.953, and was used to assay for tubal extension as a function of increasing VEGF
concentration. Measurements of NE, node number, chord lengths were demonstrated on ECs network-like
patterns in culture.
Numerous investigations in the last years focused on chromosome and gene arrangements through the application of
statistical methods that analyze the non randomness of spatial distributions of fluorescence in situ hybridization (FISH)
labeled nucleic acid sequences in terms of their distance to the nuclear centers and their proximity to each other.
However, existing imaging processing methods are rather limited in extracting sufficient number of nuclei with FISH
label sequences, and manual analysis is unreasonably time-consuming and subjective. This paper presents an automated
system that integrates a series of advanced image processing methods to over come this rate-limiting step. Evaluation
results show that the proposed method is efficient, robust, and effective in extracting individual nuclei with FISH labels.
A mathematical model was developed to predict the bi- directional transport rate of fluorescent proteins across the nuclear membrane during a fluorescence recovery after photobleaching (FRAP) experiment. The model assumes that the total amount of fluorescent protein remains the same in the cell (i.e. no production, loss or exchange with the outside of the cell) and that the cell is in a state of equilibrium; i.e. proteins are leaving and entering the nucleus at an equal rate. The latter assumption has the advantage of not needing to take into account the method of protein transport (e.g. active or passive). The model includes correction for the photobleaching that happens during image acquisition following the deliberate photobleach. In this study, the green fluorescent protein (GFP) was transfected into cells in order to study its free behavior. In the FRAP experiments, either the entire nucleus and part of the cytoplasm or only part of the cytoplasm was photobleached followed by time-series imaging of the fluorescence redistribution. The model was fitted to the curves of intensity loss or recovery after photobleaching using numerical, non-linear methods. In addition, the mobile fractions of free GFP in the cytoplasm and the nucleus could be determined.
Advancements in image analysis shave recently made it possible to segment the cells and nuclei, of a wide variety of tissues, from 3D images collected using fluorescence confocal microscopy. This has made it possible to analyze the spatial organization of individual cells and nuclei within the natural tissue context. We present here a spatial statistical method which examines an arbitrary 3D distribution of cells of two different types and determines the probability that the cells are randomly mixed, cells of one type are clustered, or cells of different types are preferentially associated. Beginning with a segmented 3D image of cells, the Voronoi diagram is calculated to indicate the nearest neighbor relationships of the cells. Then, in a test image of the same topology, cells are randomly assigned a type in the same proportions as in the actual specimen and the ratio of cells with nearest neighbors of the same type versus the other types is calculated. Repetition of this random assignment is used to generate a distribution function which is specific for the tissue image. Comparison of the ratios for the actual sample to this distribution assigns probabilities for the conditions defined above. The technique is being used to analyze the organization of genetically normal versus abnormal cells in cancer tissue.
The molecular and structural analysis of cells within their tissue context helps us understand disease mechanisms, such as carcinogenesis. Standard analysis of cutting specimens into thin (4 micrometer) sections, followed by labeling and visual microscopic analysis, has the limitation that tissue properties can only be studied within the section plane, and not perpendicular to the plane. We solved these limitations by building a system for registering images of adjacent sections. In addition, the system enables analysis of many molecular markers in a specific tissue volume, by labeling different sections with different markers, followed by using the system to locate the relevant tissue volume in each section. The system has three stages. First, it automatically images each entire section and two fiducial markers per slide. After this stage, the slides can be removed from the microscope. In stage two, pairs of images of adjacent sections are registered. This is done by interactively marking several points that are common to both images, which are used to calculate the translation and rotation of the images relative to each other. Different registrations can be performed on different parts of the images to account for differential stretching, tearing and folding of sections. In stage three, a slide is placed on the microscope stage and the analyst can bring a specific location into the field of view by referring to it in the previously acquired image. Accuracy is approximately equal to 10 micrometers.
KEYWORDS: 3D image processing, 3D displays, Algorithm development, Information operations, Silicon, Tissues, Confocal microscopy, Acquisition tracking and pointing, Breast cancer, Evolutionary algorithms
Fluorescence in situ hybridization (FISH) is useful for analyzing specific nucleic acid sequences in individual cells. Its application to tissue sections has been limited however because of the difficulties of performing the hybridization and analysis in sections that are thick enough to contain intact nuclei. Recent improvements in FISH permit hybridization with chromosome-specific, centromeric probes throughout 20 micrometers formalin fixed, paraffin- embedded sections, which do contain many intact nuclei. This paper describes software to facilitate analysis of these 3D hybridizations. We have developed two algorithms for analyzing 3D, confocal images of thick sections. One displays 2D, maximum-intensity, projection images through the original 3D image at different angles. When projections are viewed sequentially, the 3D image appears semi-transparent and rotates. The second algorithm allows interactive enumeration of FISH signals. Each signal is marked by the analyst. Then, for each pair of marked signals, a 2D slice image along the line connecting both marked signals and parallel to the z (depth) axis is displayed. From this slice, the analyst decides if the signals are in the same or different nuclei, or if the signals should be rejected because they are in a nucleus truncated by the upper or lower surface of the section. After consideration of all pairs of signals, the algorithm produces a map of the tissue section showing the numbers of signals in each of the intact nucleus. The algorithms enable analysis of small, premalignant and early malignant lesions and infiltrative lesions that cannot be analyzed by other molecular techniques and permit the direct correlation of FISH information with histology/cytology.
To reconstruct the object from its observed images, the characteristics of the imaging system must first be obtained. In a microscope imaging system, the characteristics vary not only in the imaging plane, but also vary as a function of the focus in which the image is taken. Thus, a three dimensional system response or point spread function (PSF) needs to be determined. One way of determining the PSF is to use a theoretical approach to analyze the aberration-free microscope imaging system. However, the assumptions and properties of lenses in the system are often not ideal. Thus an experimental approach for determining the PSF is sometimes used. We report on the results of our experiments where some of the problems associated with the determination of the experimental PSF are overcome. Point source objects are hard to find in nature. In our analysis, we use test objects which simulates point sources, (such as small fluorescence beads,) and objects which can be described as a convolution of the PSF with their shape (such as a step edge). To increase the spatial resolution, the precise location of the object is also estimated to a fraction of a pixel. The results of this is then compared with the theoretical approach.
The analysis of fluorescent stained clusters of cells has been improved by recording multiple images of the same microscopic scene at different focal planes and then applying a three dimensional (3-D) out of focus background subtraction algorithm. The algorithm significantly reduced the out of focus signal and improved the spatial resolution. The method was tested on specimens of 10 micrometers diameter ((phi) ) beads embedded in agarose and on a 5 micrometers breast tumor section labeled with a fluorescent DNA stain. The images were analyzed using an algorithm for automatically detecting fluorescent objects. The proportion of correctly detected in focus beads and breast nuclei increased from 1/8 to 8/8 and from 56/104 to 81/104 respectively after processing by the subtraction algorithm. Furthermore, the subtraction algorithm reduced the proportion of out of focus relative to in focus total intensity detected in the bead images from 51% to 33%. Further developments of these techniques, that utilize the 3-D point spread function (PSF) of the imaging system and a 3-D segmentation algorithm, should result in the correct detection and precise quantification of virtually all cells in solid tumor specimens. Thus the approach should serve as a highly reliable automated screening method for a wide variety of clinical specimens.
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