General Electric Company

Leica Microsystems Cms Gmbh

Ge Healthcare Bio-Sciences Corp.

Xiaofeng Liu

Ek Tsoon Tan

John F Schenck

Albert Amos Montillo

Robert David Darrow

Michael John Gerdes

Alex David Corwin

Shai Dekel

Dan Xu

Ileana Hancu

Kevin Bernard Kenny

Dirk Ryan Padfield

Eric William Fiveland

Fiona Ginty

Alexandre Nikolov Iankoulski

Sohan Rashmi Ranjan

Alexander Sherman

Stephen Eric Zingelewicz

David LaVan Henderson

Viswanath Avasarala

Tianyi Wang

Antti Seppo

Ainura Kyshtoobayeva

Denise A Hollman-Hewgley

Ying Li

Natalia R Jun

Thomas Ha

Adriana Ines Larriera

Antti E Seppo

David Lavan Henderson

Adriana I Larriera Moreno

Natalie R Jun

Kevin B Kenny

Stephen E Zingelewicz

Ainura Kyshtoobayeve

A method for generating an image of a region of interest in a biological sample comprising the steps of: generating a first image including the region of interest of the biological sample having undergone a first protocol but not a second protocol; and generating a second image including the region of interest of the biological sample after having undergone a second protocol; wherein the region of interest is smaller than said sample. Also provided is a method of analyzing a biological sample, comprising providing an image of the biological sample according to the method for generating an image of a region of interest in a biological sample, and analyzing the biological sample from the image. Further provided are system and kit that comprise the means for executing the novel methods.

Methods for generating an image of a biological sample

The system and method of the invention pertains to an MR-guided breast biopsy procedure, specifically as to quickly identifying the biopsy location. More particularly, the system utilizes a diagnostic imaging modality such as magnetic resonance imaging (MRI) to locate one or more lesions in a human breast. Non-rigid registration between uncompressed screening images (where the lesion has been previously identified) and the compressed biopsy images enables easier identification of the biopsy site, hence shortening the biopsy procedure.

Computer-aided lesion detection and track planning for MRI-guided breast biopsy

A method implemented using at least one processor includes receiving a target image and a reference image. The target image is a distorted magnetic resonance image and the reference image is an undistorted magnetic resonance image. The method further includes selecting an image registration method for registering the target image to the reference image, wherein the image registration method uses an image transformation. The method further includes performing image registration of the target image with the reference image, wherein the image registration provides a plurality of optimized parameters of the image transformation. The method also includes generating a corrected image based on the target image and the plurality of optimized parameters of the image transformation.

System and method for medical image correction

The system and method of the invention combines target image intensity into a maximum likelihood estimate (MLE) framework as in STAPLE to take advantage of both intensity-based segmentation and statistical label fusion based on atlas consensus and performance level, abbreviated iSTAPLE. The MLE framework is then solved using a modified expectation-maximization algorithm to simultaneously estimate the intensity profiles of structures of interest as well as the true segmentation and atlas performance level. The iSTAPLE greatly extends the use of atlases such that the target image need not have the same image contrast and intensity range as the atlas images.

Systems and methods for image segmentation using target image intensity

Systems and methods for image segmentation using a deformable atlas are provided. One method includes obtaining one or more target images, obtaining one or more propagated label probabilities for the one or more target images, and segmenting the one or more target images using a cost function of a deformable atlas model. The method further includes identifying segmented structures within the one or more target images based on the segmented one or more target images.

Systems and methods for image segmentation using a deformable atlas

The present techniques provide for the evaluation of cellular motion and/or cellular properties based on an analysis of motion for cluster of cells. In an exemplary technique, images of cells are acquired and the image is segmented into clusters. Motion data for each respective cluster is derived from the segmented data. The properties of each cluster can be used to evaluate cellular properties and/or cellular motion properties.

Automatic segmentation and characterization of cellular motion

Certain embodiments of the present invention provide methods and systems for determining a hanging protocol for display of clinical images in a study. Certain embodiments provide a machine learning hanging protocol analysis system. The example system includes an image processing module to process image data to provide one or more features. The example system includes a learning engine to receive processed image data and additional data to learn and adapt a hanging protocol for repeated use by applying one or more machine learning algorithms to the processed image data and additional data. The learning engine is to continue to refine an available selection of candidate layouts based on the processed image data and additional data to provide one or more layout choices for selection to form a hanging protocol for display of image and other data.

Smart PACS workflow systems and methods driven by explicit learning from users

Growing community of inventors

Niskayuna, NY, United States of America

Xiaofeng Liu