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 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: 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: 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: 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.