Abstract: Disclosed herein, in certain embodiments, is a method of treating breast cancer characterized by the amplification of HER2 genes in a subject in need thereof, comprising: (a) isolating a tissue sample comprised of a plurality of breast tumor cells; (b) isolating a first section from said tissue sample; (c) isolating a second section from an adjacent portion of said tissue sample; (d) contacting the first section with a first stain; (e) contacting the second section with a probe; (f) imaging the first section following contact with the stain to produce a first image; (g) analyzing the first image for abnormal microscopic features; (h) identifying areas of interest in the first image that display abnormal microscopic features; (i) electronically annotating the first image to identify the areas of interest; (j) imaging the second section following contact with the probe; (k) aligning the first image and the second image; and (l) analyzing areas of interest in the second image that correspond to an area of intere

Abstract: 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.

Abstract: An automated method and system for method and system for indexing, storing and searching medical images using indexed anatomical structures of interest. The method and system provides automated screening of medical images using anatomical structures of interest using a pre-determined hierarchical indexing and searching scheme.

Abstract: Provided herein are systems methods including a design of a microscope slide scanner for digital pathology applications which provides high quality images and automated batch-mode operation at low cost. The instrument architecture is advantageously based on a convergence of high performance, yet low cost, computing technologies, interfaces and software standards to enable high quality digital microscopy at very low cost. Also provided is a method based in part on a stitching method that allows for dividing an image into a number of overlapping tiles and reconstituting the image with a magnification without substantial loss of accuracy. A scanner is employed in capturing snapshot images. The method allows for overlapping images captured in consecutive snapshots.

Abstract: An automated method and system for method and system for indexing, storing and searching medical images using indexed anatomical structures of interest. The method and system provides automated screening of medical images using anatomical structures of interest using a pre-determined hierarchical indexing and searching scheme.

Abstract: A method and system method and system automated digital image analysis of prostrate neoplasms using morphologic patterns. The method and system provide automated screening of prostate needle biopsy specimens in a digital image and automated diagnosis of prostatectomy specimens.

Abstract: 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.

Abstract: A method and system for automated detection of immunohistochemical (IHC) patterns. The method and system is able to automatically differentiate an epithelial cell part from a non-epithelial cell part of a digital image of a tissue sample to which an IHC compound has been applied. The method and system help to automatically correct errors made in a manual interpretation of a pathological or other manual analysis of tissue samples for diseases such as a human cancer.

Abstract: A method and system for morphology based mitosis identification and classification of digital images. Luminance parameters such as intensity, etc. from a digital image of a biological sample (e.g., tissue cells) to which a chemical compound (e.g., a marker dye) has been applied are analyzed and corrected if necessary. Morphological parameters (e.g., size, elongation ratio, parallelism, boundary roughness, convex hull shape, etc.) from individual components within the biological sample are analyzed. A medical conclusion (e.g., type and count of mitotic cells) or a life science and biotechnology experiment conclusion is determined from the analyzed luminance and morphological parameters. The method and system may be used to develop applications for automatically obtaining a medical diagnosis (e.g., a carcinoma diagnosis).

Abstract: A method and system for automated digital fluorescent in situ hybridization (FISH) image analysis. Luminance parameters from a digital image of a biological tissue sample to which a fluorescent compound (e.g., LSI-HER-2/neu and CEP-17 dyes) have been applied are analyzed to determine plural regions of interest. Fluorescent color signals in the plural regions of interest including plural cell nuclei are identified, classified and grouped into plural groups. Each of the plural groups is validated based on pre-defined conditions. A medical diagnosis or prognosis or medical, life science or biotechnology experiment conclusion determined using a count of plural ratios of validated fluorescent color signals within each of the cell nuclei within the plural groups.

Abstract: A method and system for automatically determining diagnostic saliency of digital images for medical and/or pathological purposes. Luminance parameters (e.g. intensity, etc.) from a digital image of a biological sample (e.g., tissue cells) to which a chemical compound (e.g., a marker dye) has been applied are automatically analyzed and automatically corrected if necessary. Morphological parameters (e.g., cell membrane, cell nucleus, mitotic cells, etc.) from individual components within the biological sample are automatically analyzed on the digital image. A medical conclusion (e.g., a medical diagnosis or prognosis) is automatically determined from the analyzed luminance and morphological parameters.

Abstract: 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.