Siemens Corporation

Siemens Aktiengesellschaft

Beth Israel Deaconess Medical Center, Inc.

Siemens Healthcare Gmbh

Siemens Medical Solutions Usa, Inc.

Parmeshwar Khurd

Ali Kamen

Leo John Grady

Berthold Kiefer

John V Frangioni

Martin Requardt

Hari Sundar

Clifford R Weiss

Mamadou Diallo

Dorin Comaniciu

Ankur Kapoor

Ralf Nanke

Gianluca Paladini

Philip Mewes

Jens Fehre

Peter Mountney

Wolfgang Wein

Peter Gall

Oliver Kutter

Sofiene Jenzri

Maximilian Allan

Summer Gibbs-Strauss

Rafiou Oketokoun

Tejas Gajera

Kalpitkumar Gajera

In a method for image guided prostate cancer needle biopsy, a first registration is performed to match a first image of a prostate to a second image of the prostate. Third images of the prostate are acquired and compounded into a three-dimensional (3D) image. The prostate in the compounded 3D image is segmented to show its border. A second registration and then a third registration different from the second registration is performed on distance maps generated from the prostate borders of the first image and the compounded 3D image, wherein the first and second registrations are based on a biomechanical property of the prostate. A region of interest in the first image is mapped to the compounded 3D image or a fourth image of the prostate acquired with the second modality.

System and method for image guided prostate cancer needle biopsy

A method and system for integrating radiological and pathological information for cancer diagnosis, therapy selection, and monitoring is disclosed. A radiological image of a patient, such as a magnetic resonance (MR), computed tomography (CT), positron emission tomography (PET), or ultrasound image, is received. A location corresponding to each of one or more biopsy samples is determined in the at least one radiological image. An integrated display is used to display a histological image corresponding to the each biopsy samples, the radiological image, and the location corresponding to each biopsy samples in the radiological image. Pathological information and radiological information are integrated by combining features extracted from the histological images and the features extracted from the corresponding locations in the radiological image for cancer grading, prognosis prediction, and therapy selection.

Method and system for integrated radiological and pathological information for diagnosis, therapy selection, and monitoring

Deformable, anatomical trees represented by scan data from different times are matched. Coherent point drift (CPD) solved using expectation maximization is enhanced with tangent or other curve information. By including point-curve information, another characteristic than GMM-based probabilities are included in the cost function for matching. The angle information provided by the tangent, normal, or other point-curve measure may more likely match points in one set representing a tree to points in another set representing the tree.

Deformable tree matching with tangent-enhanced coherent point drift

A computer-implemented method of performing a combined surface reconstruction and registration of stereo laparoscopic images includes a computer system generating an intraoperative three-dimensional model of an anatomical area of interest and receiving a plurality of stereo endoscopic images of the anatomical area of interest from a laparoscope. The computer system performs a first stereo reconstruction process using the stereo endoscopic images to yield a first surface image corresponding to the anatomical area of interest and aligns the first surface image with a surface of the intraoperative three-dimensional model using a rigid registration process. Then, the computer system performs a second stereo reconstruction process using the first surface image and prior knowledge from a pre-operative three dimensional model to yield a second surface image corresponding to the anatomical area of interest and aligns the second surface image with the surface of the intraoperative three-dimensional model using a non-rigid registration process.

Combined surface reconstruction and registration for laparoscopic surgery

Methods for analyzing biomedical data include: (a) obtaining macroscopic imaging data; (b) obtaining histopathological imaging data; (c) executing a parallel algorithm stored on a non-transient computer-readable medium to compute one or a plurality of network cycle features of a relative neighborhood graph derived from the histopathological imaging data; (d) registering the macroscopic imaging data and the histopathological imaging data; and (e) correlating the macroscopic imaging data and the network cycle features. Systems for analyzing biomedical data and computer readable storage media are described.

Network cycle features in relative neighborhood graphs

An image mosaicking method includes performing pairwise registration of a plurality of tiles (), determining absolute homographies for each of the plurality of tiles according to the pairwise registration (B), and performing a blending of the plurality of tiles to obtain a stitched image according to the absolute homographies ().

Global error minimization in image mosaicking using graph laplacians and its applications in microscopy

Automatic prostate localization in T2-weighted MR images facilitate labor-intensive cancer imaging techniques. Methods and systems to accurately segment the prostate gland in MR images are provided and address large variations in prostate anatomy and disease, intensity inhomogeneities, and artifacts induced by endorectal coils. A center of the prostate is automatically detected with a boosted classifier trained on intensity based multi-level Gaussian Mixture Model Expectation Maximization (GMM-EM) segmentations of the raw MR images. A shape model is used in conjunction with Multi-Label Random Walker (MLRW) to constrain the seeding process within MLRW.

Systems and method for automatic prostate localization in MR images using random walker segmentation initialized via boosted classifiers

Growing community of inventors

Princeton, NJ, United States of America

Parmeshwar Khurd