Abstract: In an automated method of determining potential efficacy of immunotherapy approaches, an automated slide scanning system with an image acquisition unit is provided. A sample slide with a mounted tissue sample is received by the automated slide scanning system. One or more slide images of the tissue sample are acquired with the image acquisition unit. At least one cell segmentation process is executed on the slide image in order to identify tissue cells. The cells are analyzed with at least one cell classification algorithm in order to identify a tumor cell percent positivity value and an immune cell percent positivity value, and a treatment efficacy score is subsequently calculated. Further features provided include pixel to pixel image registration, reconstruction, composite multi-layer viewing, and other image management tools.

Abstract: A smart microscope slide for concurrent storage of a specimen and associated digital data consists of a data storage device and a microscope slide which are connected adjacent to each other. The data storage device and the microscope slide are connected together through a structural body. Digital data associated with a specimen may be stored in the same physical object as the specimen mounted to the slide, thus consolidating the physical specimen and the digital information in one place for ease of access.

Abstract: A tissue area detection method in digital pathology imaging uses an automated method to detect tissue area of interest (AOI detection) on a Whole Slide Analysis (WSA) or Tissue Micro Array (TMA) thumbnail image. The present method may use preprocessing of the image followed by a two-pass segmentation technique for separating tissue areas from non-tissue areas. The present method may further use global and local window statistics for thresholding to overcome variations in staining intensity that may hamper accurate selection of the area of interest. The present method may also have a classifying process of the tissue area based on the staining method used and applying stain specific filters to remove unwanted artifacts.

Abstract: A slide storage, retrieval, transfer and scanning system for a slide scanner presents multiple components which work together in an automated fashion to streamline slide scanning in digital pathology. A slide storage assembly stores several slide baskets and can deliver a particular basket through a rotation mechanism. A slide basket transfer assembly retrieves the basket from the slide storage assembly and transfers the basket to a secondary location, where a slide transfer assembly retrieves a single slide from the basket and delivers the slide to a slide scanning stage for scanning.

Abstract: In an automated method of determining potential efficacy of immunotherapy approaches, an automated slide scanning system with an image acquisition unit is provided. A sample slide with a mounted tissue sample is received by the automated slide scanning system. One or more slide images of the tissue sample are acquired with the image acquisition unit. At least one cell segmentation process is executed on the slide image in order to identify tissue cells. The cells are analyzed with at least one cell classification algorithm in order to identify a tumor cell percent positivity value and an immune cell percent positivity value, and a treatment efficacy score is subsequently calculated. Further features provided include pixel to pixel image registration, reconstruction, composite multi-layer viewing, and other image management tools.

Abstract: A slide storage, retrieval, transfer and scanning system for a slide scanner presents multiple components which work together in an automated fashion to streamline slide scanning in digital pathology. A slide storage assembly stores several slide baskets and can deliver a particular basket through a rotation mechanism. A slide basket transfer assembly retrieves the basket from the slide storage assembly and transfers the basket to a secondary location, where a slide transfer assembly retrieves a single slide from the basket and delivers the slide to a slide scanning stage for scanning.

Abstract: A tissue area detection method in digital pathology imaging uses an automated method to detect tissue area of interest (AOI detection) on a Whole Slide Analysis (WSA) or Tissue Micro Array (TMA) thumbnail image. The present method may use preprocessing of the image followed by a two-pass segmentation technique for separating tissue areas from non-tissue areas. The present method may further use global and local window statistics for thresholding to overcome variations in staining intensity that may hamper accurate selection of the area of interest. The present method may also have a classifying process of the tissue area based on the staining method used and applying stain specific filters to remove unwanted artifacts.

Abstract: A smart microscope slide for concurrent storage of a specimen and associated digital data consists of a data storage device and a microscope slide which are connected adjacent to each other. The data storage device and the microscope slide are connected together through a structural body. Digital data associated with a specimen may be stored in the same physical object as the specimen mounted to the slide, thus consolidating the physical specimen and the digital information in one place for ease of access.

Abstract: A digital pathology imaging method may comprise receiving a plurality of image sections on a cloud server, the plurality of image sections being a result of splitting an initial digital image; stitching the plurality of image sections on the cloud server into a reconstituted digital image; and providing access to the reconstituted digital image. A system for digital pathology imaging may comprise a cloud server for receiving a plurality of image sections, wherein the cloud server comprises an image stitching module configured to stitch the plurality of image sections into a reconstituted digital image.

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 quantitation of tissue micro-array image (TMA) digital analysis. The method and system automatically analyze a digital image of a TMA with plural TMA cores created using a needle to biopsy or other techniques to create standard histologic sections and placing the resulting needle cores into TMA. The automated analysis allows a medical conclusion such as a medical diagnosis or medical prognosis (e.g., for a human cancer) to be automatically determined. The method and system provides reliable automatic TMA core gridding and automated TMA core boundary detection including detection of overlapping or touching TMA cores on a grid.

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.