University of Minnesota

Ethan Yize Leng

Gregory John Metzger

Stephen C Schmechel

Joseph S Koopmeiners

Jonathan Henriksen

David Henry Porter

Medical imaging analysis systems are configured to perform automatic image registration algorithms that perform three-dimensional (3D), affine, and/or intensity-based co-registration of magnetic resonance imaging (MRI) data, such as multiparametric MRI (mpMRT) data, using mutual information (MI) as a similarity metric. An apparatus comprises a computer-readable storage medium storing a plurality of imaging series of magnetic resonance imaging (MRI) data for imaged tissue of a patient; and a processor coupled to the computer-readable storage medium. The processor is configured to receive the imaging series of MRI data; identify a volume of interest (VOI) of each image of the imaging series of MRI data; compute registration parameters for the VOIs through the maximization of mutual information of the corrected VOIs; and register the VOIs using the computed registration parameters.

Automated co-registration of prostate MRI data

Automated, machine learning-based systems are described for the analysis and annotation (i.e., detection or delineation) of prostate cancer (PCa) on histologically-stained pathology slides of prostatectomy specimens. A technical framework is described for automating the annotation of predicted PCa that is based on, for example, automated spatial alignment and colorimetric analysis of both H&E and IHC whole-slide images (WSIs). The WSIs may, as one example, be stained with a particular triple-antibody cocktail against high-molecular weight cytokeratin (HMWCK), p63, and &#x3b1;-methylacyl CoA racemase (AMACR).

Growing community of inventors

Minneapolis, MN, United States of America

Ethan Yize Leng