Abstract: The disclosure concerns a method for assessing muscular dystrophy-linked protein expression in muscle fibers using digital image analysis of tissue. The method relates to assessing disease severity in individuals with muscular dystrophy. Muscle tissue samples are obtained from patients submitted for evaluation and processed to produce tissue sections mounted on glass slides which have been stained for a muscular dystrophy-linked protein. Digital images of the stained tissue sections are generated and analyzed by applying an algorithm process implemented by a computer to the images. The algorithm process extracts the morphometric and staining features of the muscular dystrophy-linked protein staining in the tissue, and parameters relating to these features are used to score the disease status for each patient submitted for evaluation.

Abstract: The disclosure concerns a method for patient stratification and selection of patients who are candidates for a specific therapy is described which is based on quantifying one or more digital image analysis feature distributions from stained tissue. The method extends beyond the abilities of a manual observer and a microscope, and generally comprises: acquiring digital images of stained tissue sections from patients submitted for evaluation, applying an algorithm process to said images with a computer to extract the morphometric and staining features of image pixels and tissue objects, deriving one or more distribution function for one or more image analysis features, calculating a summary statistic of the one or more distribution functions, and using said summary statistic along with an associated predefined patient stratification paradigm to separate a patient cohort into distinct strata which correspond to a decision to include or exclude a patient for a specific therapy.

Abstract: Staining of tissue by is a common approach utilized to visualize a gene product in tissue context. In certain applications, it is necessary to report a sum of events within the tissue as a specific function of the target tissue area, which is a sub-area of the total tissue, as a normalization factor for reporting the quantification. Here, we describe methods of determining target tissue area and reporting a quantification which is ratiometric to the target tissue area, utilizing computer algorithms. It is important to assign a value for the “target tissue area” in scenarios where a tissue area normalization factor needed in the most pathologically relevant fashion during the application of tissue image analysis. We have created methods for determining and reporting “target tissue area” as normalization factor which are useful in diagnostic applications utilizing image analysis.

Abstract: The invention concerns automated methods for assessing tissue morphometry in digital images of tissue sections derived from small intestine biopsy samples from patients submitted for evaluation of celiac disease. The methods generally involve digital image analysis of tissue section images, and specifically involve post-processing each image to produce a binary mask capturing the tissue area footprint on the glass slide. Virtual stereology probes are placed on each image and assessed to estimate the ratio of the surface area to volume of the tissue specimen. The surface area to volume ratio is used to diagnose celiac disease and make inferences about the severity of celiac disease in those individuals with a positive diagnosis of celiac disease.

Abstract: The instant disclosure describes a method for measuring and reporting vascularity in a biological tissue sample. The method generally includes: within a digital image of a tissue section, (i) identifying endothelial cells, lymphatic cells, or a combination thereof; (ii) mapping one or more proximity regions, each of the proximity regions defining an area between detected vessels and a first distance outwardly therefrom; and (iii) calculating one or more of: a vessel proximity score or a hypoxia score, wherein the vessel proximity score relates a composition of objects within the proximity regions, and wherein the hypoxia score relates a composition of tissue within or outside of the proximity regions, respectively.

Abstract: Feature analysis on consecutive tissue sections (FACTS) includes obtaining a plurality of consecutive tissue sections from a tissue sample, staining the sections with at least one biomarker, obtaining a digital image thereof, and identifying one or more regions of interest within a middle of the consecutive tissue sections. The digital images of the consecutive tissue sections are registered for alignment and the one or more regions of interest are transferred from the image of the middle section to the images of the adjacent sections. Each image of the consecutive tissue sections is then analyzed and scored as appropriate. Using FACTS methods, pathologist time for annotation is reduced to a single slide. Optionally, multiple middle sections may be annotated for regions of interest and transferred accordingly.

Abstract: Data processing systems and methods are provided that improve the perceived efficiency of image analysis tools in a digital pathology workflow when challenged by analyzing large size microscopic images. Various embodiments utilize a separation of the image analysis programs into a first automatically executed segment that requires no user interactions for identification and quantification of object data of processed images, and a second interactive segment for accepting user input. User interactions are only applied to the already calculated object data resulting in an improved system response time. A tile-based processing scheme with concurrent processing and generation of object data in a tile-based data format provides further performance improvements of the system.

Abstract: Methods for cell-based tissue analysis utilize modern trends in digital microscopy to obtain, process, calibrate, and analyze digital images of tissue sections to quantify cell-based data for improved histological analysis. Using data from multiple images of a common tissue section, or data from images of multiple tissue sections, additional degrees of freedom are realized and the resulting analysis provides added depth to histological analysis of tissue samples. With computerized analytical methods, speed and accuracy of histological analysis is greatly improved.

Abstract: Systems and methods relating to a cell-based pattern recognition tool for microscopy images from tissue sections are described, wherein cell features are extracted and a classifier is built in accordance with a particular application using an interactive training tied to a computerized platform, the result is an application-specific classifier that further processes images in accordance with the specific application, thereby tuning an automated process for cell based pattern recognition.

Abstract: A method for a continuous scoring scheme used for the assessment of biomarker expressions in the analysis of tissue sections, and digital images thereof, is based on cell classifications.

Abstract: This disclosure concerns methods for evaluating inflammatory cells and modulators of the inflammatory response in tumor tissue and other relevant tissue types. The methods entail: obtaining a tissue sample and processing said tissue sample to produce histologic slides of tissue sections; staining of the tissue sections to identify inflammatory cells and modulators of the inflammatory response; digitizing slides to produce an image of the stained tissue sections; digitally stratifying the tissue sample into tumor and other relevant tissue compartments; and using digital image analysis to quantify cell-based and cell population-based features. The quantification of cell-based and cell population-based features within a tissue compartment of interest is used to develop a summary score of the immune system-tissue compartment of interest interaction. Patient stratification and selection as candidates for a therapeutic approach is ultimately based on the summary score value.