1009results about "Acquiring/recognising microscopic objects" patented technology

A method of in situ analysis of a biological sample comprising the steps of (a) staining the biological sample with N stains of which a first stain is selected from the group consisting of a first immunohistochemical stain, a first histological stain and a first DNA ploidy stain, and a second stain is selected from the group consisting of a second immunohistochemical stain, a second histological stain and a second DNA ploidy stain, with provisions that N is an integer greater than three and further that (i) if the first stain is the first immunohistochemical stain then the second stain is either the second histological stain or the second DNA ploidy stain; (ii) if the first stain is the first histological stain then the second stain is either the second immunohistochemical stain or the second DNA ploidy stain; whereas (iii) if the first stain is the first DNA ploidy stain then the second stain is either the second immunohistochemical stain or the second histological stain; and (b) using a spectral data collection device for collecting spectral data from the biological sample, the spectral data collection device and the N stains are selected such that a spectral component associated with each of the N stains is collectable.

A spectral imaging method for simultaneous detection of multiple fluorophores aimed at detecting and analyzing fluorescent in situ hybridizations employing numerous chromosome paints and/or loci specific probes each labeled with a different fluorophore or a combination of fluorophores for color karyotyping, and at multicolor chromosome banding, wherein each chromosome acquires a specifying banding pattern, which pattern is established using groups of chromosome fragments labeled with various fluorophore or combinations of fluorophores.

The invention provides systems and methods for detection, grading, scoring and tele-screening of cancerous lesions. A complete scheme for automated quantitative analysis and assessment of human and animal tissue images of several types of cancers is presented. Various aspects of the invention are directed to the detection, grading, prediction and staging of prostate cancer on serial sections/slides of prostate core images, or biopsy images. Accordingly, the invention includes a variety of sub-systems, which could be used separately or in conjunction to automatically grade cancerous regions. Each system utilizes a different approach with a different feature set. For instance, in the quantitative analysis, textural-based and morphology-based features may be extracted at image- and (or) object-levels from regions of interest. Additionally, the invention provides sub-systems and methods for accurate detection and mapping of disease in whole slide digitized images by extracting new features through integration of one or more of the above-mentioned classification systems. The invention also addresses the modeling, qualitative analysis and assessment of 3-D histopathology images which assist pathologists in visualization, evaluation and diagnosis of diseased tissue. Moreover, the invention includes systems and methods for the development of a tele-screening system in which the proposed computer-aided diagnosis (CAD) systems. In some embodiments, novel methods for image analysis (including edge detection, color mapping characterization and others) are provided for use prior to feature extraction in the proposed CAD systems.

The subject disclosure presents systems and computer-implemented methods for automatic immune cell detection that is of assistance in clinical immune profile studies. The automatic immune cell detection method involves retrieving a plurality of image channels from a multi-channel image such as an RGB image or biologically meaningful unmixed image. A cell detector is trained to identify the immune cells by a convolutional neural network in one or multiple image channels. Further, the automatic immune cell detection algorithm involves utilizing a non-maximum suppression algorithm to obtain the immune cell coordinates from a probability map of immune cell presence possibility generated from the convolutional neural network classifier.

In accordance with preferred embodiments of the present invention, a method for imaging tissue, for example, includes the steps of mounting the tissue on a computer controlled stage of a microscope, determining volumetric imaging parameters, directing at least two photons into a region of interest, scanning the region of interest across a portion of the tissue, imaging a plurality of layers of the tissue in a plurality of volumes of the tissue in the region of interest, sectioning the portion of the tissue and imaging a second plurality of layers of the tissue in a second plurality of volumes of the tissue in the region of interest, detecting a fluorescence image of the tissue due to said excitation light; and processing three-dimensional data that is collected to create a three-dimensional image of the region of interest.

A color display comprising an image of all chromosomes or portions of chromosomes of a cell, each of the chromosomes or portions of chromosomes being painted with a different fluorophore or a combination of fluorophores, the image presenting the chromosomes or portions of chromosomes in different distinctive colors, wherein each of the chromosomes or portions of chromosomes is associated with one of the different distinctive colors.

The methods, devices and systems may allow a practitioner to obtain information regarding a biological sample, including analytical data, a medical diagnosis, and/or a prognosis or predictive analysis. In addition, the methods, devices and systems may train one or more machine learning algorithms to perform a diagnosis of a biological sample.

Methods and apparatus are provided for the automated analysis of images of living cells acquired by time-lapse microscopy. The new methods and apparatus can be used for the segmentation, classification and tracking of individual cells in a cell population, and for the extraction of biologically significant features from the cell images. Based upon certain extracted features, the inventive image analysis methods can characterize a cell as mitotic or interphase and/or can classify a cell into one of the following mitotic phases: prophase, metaphase, arrested metaphase, and anaphase with high accuracy.

A method and system for automatic digital image based tissue independent simultaneous nucleus, cytoplasm and membrane quantitation. Plural types of pixels comprising cell components including at least cell cytoplasm and cell membranes from a biological tissue sample to which a chemical compound has been applied and has been processed to remove background pixels and pixels including counterstained components are simultaneously identified. The identified cell components pixels are automatically classified to determine a medical conclusion such as a human breast cancer, a human prostrate cancer or an animal cancer.

Systems and methods for rapidly analyzing cell containing samples, for example to identify morphology or to localize and quantitate biomarkers are disclosed.

Disclosed herein are systems, components, devices and methods for automated yeast pedigree analysis. Systems, components, devices and methods for analyzing microorganisms, cells, particles and molecules are also disclosed.

A method is provided for comparing multiple samples of cell extract containing a plurality of components. The method comprises the steps of preparing at least two samples of cell extract from at least two groups of cells and of exposing each of said sample of said cell extract to a different one of a set of matched markers, e.g. luminescent markers, to bind the marker to the cell extract to label the cell extract, each marker within said set of markers being capable of binding to the cell extract and can be individually detected from all other markers within said set. The samples are then mixed to form a mixture and said mixture is electrophoresed to separate the components within the cell extract. At least two electronic images of the electrophoresed mixture are obtained (I) by detection of the individual markers, each image being represented by detection of a marker different from the others. One resultant electronic image (I_res) of the obtained at least two electronic images is created (II) and analyzed in order to identify spot analysis areas (III). The identified spot analysis areas are applied on the respective at least two electronic images for evaluating said areas in order to detect spots representing components of said cell extracts (IV).

A method of spectral-morphometric analysis of biological samples, the biological samples including substantially constant components and suspected variable components, the method is effected by following the steps of (a) using a spectral data collection device for collecting spectral data of picture elements of the biological samples; (b) defining a spectral vector associated with picture elements representing a constant component of at least one of the biological samples; (c) using the spectral vector for defining a correcting function being selected such that when operated on spectral vectors associated with picture elements representing other constant components, spectral vectors of the other constant components are modified to substantially resemble the spectral vector; (d) operating the correcting function on spectral vectors associated with at least the variable components for obtaining corrected spectral vectors thereof; and (e) classifying the corrected spectral vectors into classification groups.

Provided are methods for determining and analyzing photometric and morphogenic features of small objects, such as cells to, for example, identify different cell states. In particularly, methods are provided for identifying apoptotic cells, and for distinguishing between cells undergoing apoptosis versus necrosis.

A method for remote scenes classification comprising the steps of (a) preparing a reference template for classification of the remote scenes via (i) classifying a set of reference scenes via a conventional classification technique for obtaining a set of preclassified reference scenes; (ii) using a first spectral imager for measuring a spectral cube of the preclassified reference scenes; (iii) employing a principal component analysis for extracting the spectral cube for decorrelated spectral data characterizing the reference scenes; and (vi) using at least a part of the decorrelated spectral data for the preparation of the reference template for remote scenes classification; (b) using a second spectral imager for measuring a spectral cube of analyzed remote scenes, such that a spectrum of each pixel in the remote scenes is obtained; (c) employing a decorrelation statistical method for extracting decorrelated spectral data characterizing the pixels; and (d) comparing at least a part of the decorrelated spectral data extracted from the pixels of the remote scenes with the reference template.

A method and system analyzing tissue or cell samples stored on laboratory slides or other media to identify properties of medical predictive and diagnostic relevance. The method and system includes automatically analyzing plural digital images created from plural biological tissue samples to which a staining reagent or a an immunohistochemical (IHC) compound has been applied, automatically quantitatively analyzing the relevant properties of the digital images, and creating interpretive data, images and reports resulting from such analysis.

A scanning device for biological slides is provided, which can be operated in an interactive routine mode as well as in an unsupervised high speed automatic mode. In the first case, typical components, which the pathologist is used to operating manually, such as the microscope, the stage and the focus, and which have to be motorized for the automatic unsupervised system mode, are configured to simulate manual use, operation, and response. A non-interlaced area scan camera supports the interactive selection and acquisition of individual images in the manual mode, as well as the continuous high-speed scan motion for the rare event detection and virtual slide scan applications of the system. Due to the particular requirements to accommodate both operational modes, methods are described for constructing the virtual slide out of image tiles with varying overlap areas in the x- and y-directions.

Machine executable method of analyzing growth curve data to identify the transition from a baseline phase into a growth phase. Applications of the method include analysis of results from time-dependent monitoring of amplicon synthesis in a nucleic acid amplification reaction to quantify a starting amount of a nucleic acid template in a test sample. The method advantageously simplifies the quantitation by circumventing the need to establish thresholds used for calculating initiation of the growth phase, to calculate derivatives, or to perform linear regression analysis.

A method of classifying cells into subpopulations using cell classifying data is described. The method comprises receiving and analyzing image data to identify object areas in the image data to determine, for at least one selected first cell, one or more measurements. A first parameter set is derived from the measurements for the first cell, the first parameter set comprising at least one of said one or more measurements. The first set of cells are classified into subpopulations, and identified to produce first identifying data. Cell classifying data for use in classifying a second set of cells into subpopulations is derived from the first parameter set and the first identifying data. A second set of cells is classified into subpopulations on the basis of one or more measurements taken for cells in the second set of cells, by use of the cell classifying data. The parameter sets of cells may be represented as vectors in an n-dimensional space.