Flagship Biosciences, Inc.

Trevor Johnson

Frank Voelker

Steven J Potts

Holger Lange

George David Young

Joseph Krueger

G David Young

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.

Methods for feature analysis on consecutive tissue sections

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.

Systems and methods for interactive image analysis of digital pathology images

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.

Cell-based tissue analysis

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.

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San Diego, CA, United States of America

Trevor Johnson