Siemens Healthcare Gmbh

Siemens Healthineers Ag

Siemens Aktiengesellschaft

Siemens Corporation

Mamadou Diallo

Ali Kamen

Berthold Kiefer

Bin Lou

David Jean Winkel

Tongbai Meng

Xin Yu

Dorin Comaniciu

Robert Grimm

Heinrich Von Busch

Bibo Shi

Martin Requardt

Clifford R Weiss

Parmeshwar Khurd

Nicolas Von Roden

Leo John Grady

Ralf Nanke

Jens Fehre

Wolfgang Wein

Peter Gall

Ahmet Tuysuzoglu

Sureerat Reaungamornrat

Kalpitkumar Gajera

Kareem Abdelrahman

Afshin Ezzi

Systems and methods are provided for optimizing a deep learning model. A multi-site dataset associated with different clinical sites and a deployment dataset associated with a deployment clinical site are received. A deep learning model is trained based on the multi-site dataset. The trained deep learning model is optimized based on the deployment dataset. The optimized trained deep learning model is output.

Localization and classification of abnormalities in medical images

Systems and methods for automatically registering a first input medical image and a second input medical image are provided. The first input medical image in a first modality and the second input medical image in a second modality are received. One or more objects of interest are segmented from the first input medical image to generate a first segmentation map and one or more objects of interest are segmented from the second input medical image to generate a second segmentation map. A first point cloud is extracted from the first segmentation map and a second point cloud is extracted from the second segmentation map. A transformation for aligning the first point cloud and the second point cloud is determined to register the first input medical image and the second input medical image. The transformation is output.

Automatic MR-US prostate image fusion through semi-supervised constrained learning

In an method for training artificial intelligence entities (AIE) for abnormality detection, medical imaging data of the human organ is provided as training data having training samples, the medical imaging data including imaging results from different types of imaging techniques for each training sample of the training data, a pre-trained or randomly initialized AIE is provided, and the AIE is trained using the provided training samples. The training may include, for at least one training sample, a first loss function for a sub-structure of the AIE is calculated independently of a first spatial region of the human organ, and, for a training sample, a second loss function for a sub-structure of the AIE is calculated independently of a second spatial region of the human organ. The AIE may be trained using the calculated first loss function and the calculated second loss function.

Methods and systems for improved abnormality detection in medical imaging data

Systems and methods are provided for classifying an abnormality in a medical image. An input medical image depicting a lesion is received. The lesion is localized in the input medical image using a trained localization network to generate a localization map. The lesion is classified based on the input medical image and the localization map using a trained classification network. The classification of the lesion is output. The trained localization network and the trained classification network are jointly trained.

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

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

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Plainsboro, NJ, United States of America

Mamadou Diallo