Icad, Inc.

Redwall Technologies, LLC

Kevin S Woods

Michael J Collins

Ryan McGinnis

William Pierson

Brent Woods

John E Rosenstengel

Senthil Periaswamy

Robert L Van Uitert

Eric Ridvan Üner

Kent H Hunter

James E Sabin

Embodiments of the present invention provide a method to temporally isolate data accessed by a computing device so that the data accessed by the computing device is limited to a single set of data. The method includes removing any data that is accessed by the computing device when operating in different modes so that the data is inaccessible by the computing device when operating in the mode. The method also includes switching to the mode after the data associated with the modes different from the mode have been removed. The method also includes operating in the mode based on a plurality of rules associated with the security policy in temporal isolation from any other mode associated with the computing device. The computing device is limited to operating in the mode and is prevented from accessing any data that is distinct from the single set of data of the mode.

Temporally isolating data accessed by a computing device

Systems and methods are presented that detect and classify mass-like regions exhibiting spiculated and/or dense characteristics with high sensitivity and at acceptable false positive rates. One or more suspicious masses are identified in medical imagery of the breast. In certain embodiments, a quantitative measure of spiculation and quantitative measure of density are computed for each suspicious mass located. At least one classification scheme, developed using true and false positives with similar quantitative measures, is then selected for each suspicious mass according to both quantitative measures. In certain other embodiments, a measure of breast location is computed for each suspicious mass. In one embodiment, the location determines whether a suspicious mass appears inside or outside of the parenchyma region of the breast.

Computer-aided detection and classification of suspicious masses in breast imagery

Disclosed are methods, and associated systems comprising processors, input devices and output devices, of detecting regions of interest in a tomographic breast image. The methods may comprise: acquiring tomographic breast image data; deriving a plurality of synthetic sub-volumes from the tomographic breast image data; wherein each subvolume is defined by parallel planar top and bottom surfaces; wherein planar top and bottom surfaces of successive subvolumes are parallel to each other; and wherein a top planar surface of a sub-volume is offset from a top planar surface of a prior sub-volume, such that successive sub-volumes overlap; for each sub-volume, deriving a two-dimensional image; for each two-dimensional image, identifying regions of interest therein; deriving at least one region of interest of potential clinical interest from a plurality of identified regions of interest; and outputting information associated with at least one derived region of interest of potential clinical interest.

Computer-aided detection of regions of interest in tomographic breast imagery

Methods are presented that detect and classify mass-like regions exhibiting spiculated and/or dense characteristics with high sensitivity and at acceptable false positive rates. One or more suspicious masses are identified in medical imagery of the breast. In certain embodiments, a quantitative measure of spiculation and quantitative measure of density are computed for each suspicious mass located. At least one classification scheme, developed using true and false positives with similar quantitative measures, is then selected for each suspicious mass according to both quantitative measures. In certain other embodiments, a measure of breast location is computed for each suspicious mass. In one embodiment, the location determines whether a suspicious mass appears inside or outside of the parenchyma region of the breast.

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Beavercreek, OH, United States of America

Kevin S Woods