Quality metrics for automatic evaluation of dual ish images
AssigneeVentana Medical Systems, Inc.
InventorsJeffrey Atchison, Jim Martin, Anindya Sarkar
Life of this patent
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Abstract
The present disclosure is directed to a computer system designed to (i) receive a series of images as input; (ii) compute a number of metrics derived from focus features and color separation features within the images; and (iii) evaluate the metrics to return (a) an identification of the most suitable z-layer in a z-stack, given a series of z-layer images in a z-stack; and/or (b) an identification of those image tiles that are more suitable for cellular based scoring by a medical professional, given a series of image tiles from an area of interest of a whole slide scan.
Claims17
A computer system for determining the most suitable z-layer in a z-stack of digital images, the z-stack of digital images depicting different layers of a single sample, comprising one or more processors and one or more memories, the one or more memories storing non-transitory computer-readable instructions for execution by the one or more processors to cause the one or more processors to:
- (a) compute at least one of (i) one or more focus metrics for each z-layer within the z-stack of digital images, and (ii) one or more color separation metrics for each z-layer within the z-stack of digital images;
- and (b) evaluate the computed one or more focus metrics and/or the computed one or more color separation metrics to determine a most suitable z-layer within the z-stack.
A non-transitory computer-readable medium storing computer-executable instructions, which, when executed by one or more processors, cause the one or more processors to:
- (i) compute one or more focus metrics or one or more color separation metrics for each z-layer within a z-stack of images, each z-layer within the z-stack of images corresponding to an image of a biological sample, and (ii) evaluate the focus metrics or color separation metrics to determine a most suitable z-layer within the z-stack.
Dependencies are inferred from the claim text.