Siemens Healthcare Gmbh

Siemens Medical Solutions Usa, Inc.

Siemens Aktiengesellschaft

Siemens Corporation

Siemens Healthineers Ag

Siemens Healthcare Diagnostics Gmbh

Other

Siemens Corporate Research, Inc.

Siemens Canada Limited

Siemens Ag Osterreich

National Institutes of Health

Bogdan Georgescu

Dorin Comaniciu

Yefeng Zheng

Tommaso Mansi

Ali Kamen

Puneet Sharma

Shaohua Kevin Zhou

Sasa Grbic

Vivek Kumar Singh

Razvan Ioan Ionasec

Ingmar Voigt

Xiang Sean Zhou

Saikiran Rapaka

Viorel Mihalef

Helene C Houle

Florin Cristian Ghesu

Tiziano Passerini

Hien Nguyen

David D Liu

Xiaoguang Lu

Daguang Xu

Frank Sauer

Michael Scheuering

Adrian Barbu

Dominik Neumann

Eli Gibson

Lucian Mihai Itu

Fernando Vega-Higuera

Yang Wang

Jan Boese

Shanhui Sun

Zhoubing Xu

Wen Hong Liu

Siqi Liu

R Bharat Rao

Shun P Miao

Mehmet Akif Gulsun

Noha Youssry El-Zehiry

Marie-Pierre Jolly

Razvan Ionasec

Peter Durlak

Dominik Bernhardt

Jin-hyeong Park

Arne Littmann

Youngjin Yoo

Alok Gupta

Terrence Chen

Mariappan S Nadar

Atilla Peter Kiraly

Thomas Redel

Benjamin L Odry

Li Zhang

Sriram Krishnan

Rui Liao

Edgar Mueller

Huseyin Tek

Mingqing Chen

Julian Krebs

Thomas Flohr

Jing Xiao

Ankur Kapoor

Wei Zhang

Matthias John

Daphne Yu

Klaus Engel

Kaloian Petkov

Jingdan Zhang

Constantine Simopoulos

Bimba Rao

Martin Huber

Charles Henri Florin

Peter Speier

Boris Mailhe

Bernhard Geiger

Amit Chakraborty

Michael Suehling

Estelle Camus

Gabriel Haras

Xiao Chen

Oliver Hayden

Kai Ma

Jens Gühring

Michael Kelm

Stefan Böhm

Qiu Wang

Michaela Schmidt

Andreas Keller

Sven Zuehlsdorff

Hui Xue

Anton Schick

Philipp Hoelzer

Xiaoming Bi

Christoph Guetter

Jens Guehring

Klaus Klingenbeck

Fabian Moerchen

Alexander Brost

Sebastian Vogt

Carmel Hayes

Edgar Müller

For cardiac flow detection in echocardiography, by detecting one or more valves, sampling planes or flow regions spaced from the valve and/or based on multiple valves are identified. A confidence of the detection may be used to indicate confidence of calculated quantities and/or to place the sampling planes.

Cardiac flow detection based on morphological modeling in medical diagnostic ultrasound imaging

Systems and methods for occlusion detection in medical images are provided. An input medical image of one or more vessels in an anatomical object of a patient is received. One or more anatomical landmarks are identified in the input medical image. A first patch and one or more additional patches are extracted from the input medical image based on the identified one or more anatomical landmarks. The first patch and the one or more additional patches depict different portions of the anatomical object. Features are extracted from the first patch and the one or more additional patches using a machine learning based feature extractor network. An occlusion in the one or more vessels is detected in the first patch based on the extracted features with or without modeling features on a probability distribution function. Results of the detecting are output.

Large vessel occlusion detection and classification in medical imaging

Systems and methods for determining a segmentation of a hemorrhage are provided. An input medical image of a hemorrhage of a patient is received. A contour-sensitive segmentation of the hemorrhage from the input medical image is performed using a machine learning based contour-sensitive segmentation network. A detection-sensitive segmentation of the hemorrhage from the input medical image is performed using a machine learning based detection-sensitive segmentation network. A final segmentation of the hemorrhage from the input medical image is determined based on results of the contour-sensitive segmentation and results of the detection-sensitive segmentation. The final segmentation of the hemorrhage is output.

Acute intracranial hemorrhage segmentation on brain images for volume quantification and individual detection

A computer-implemented method for detecting a failure of a device connected to a sensor is disclosed. The method includes a machine learning model receiving a trace signal from the sensor indicating a status of the device, the machine learning model encoding the trace signal into a plurality of vector representations, and the machine learning model determining whether the trace signal is valid or invalid based on the plurality of vector representations.

Automatic sensor trace validation using machine learning

Systems and method for performing a medical imaging analysis task for making a clinical decision are provided. One or more input medical images of a patient are received. A medical imaging analysis task is performed from the one or more input medical images using a machine learning based network. The machine learning based network generates a probability score associated with the medical imaging analysis task. An uncertainty measure associated with the probability score is determined. A clinical decision is made based on the probability score and the uncertainty measure.

Machine learning for automatic detection of intracranial hemorrhages with uncertainty measures from medical images

Systems and methods for generating a probabilistic tree of vessels are provided. An input medical image of vessels of a patient is received. Anatomical landmarks are identified in the input medical image. A centerline of the vessels in the input medical image is determined based on the anatomical landmarks. A probabilistic tree of the vessels is generated based on a probability of fit of the anatomical landmarks and the centerline of the vessels. The probabilistic tree of the vessels is output.

Probabilistic tree tracing and large vessel occlusion detection in medical imaging

Systems and methods for training an artificial intelligence-based system using self-supervised learning are provided. For each respective training medical image of a set of unannotated training medical images, the following steps are performed. A first augmented image is generated by applying a first augmentation operation to the respective training medical image. A second augmented image is generated by applying a second augmentation operation to the respective training medical image. A first representation vector is created from the first augmented image using an encoder network. A second representation vector is created from the second augmented image using the encoder network. The first representation vector is mapped to first cluster codes. The second representation vector is mapped to second cluster codes. The encoder network is optimized using the first and second representation vectors and the first and second cluster codes.

Self-supervised learning for artificial intelligence-based systems for medical imaging analysis

Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Method and system for image registration using an intelligent artificial agent

A 3D shape is reconstructed from a topogram. A generative network is machine trained. The generative network includes a topogram encoder for inputting the topogram and a decoder to output the 3D shape from the output of the encoder. For training, one or more other encoders are included, such as for input of a mask and/or input of a 3D shape as a regularlizer. The topogram encoder and decoder are trained with the other encoder or encoders outputting to the decoder. For application, the topogram encoder and decoder as trained, with or without the encoder for the mask and without the encoder for the 3D shape, are used to estimate the 3D shape for a patient from input of the topogram for that patient.

Three-dimensional shape reconstruction from a topogram in medical imaging

Systems and methods for generating a synthesized medical image are provided. An input medical image is received. A synthesized segmentation mask is generated. The input medical image is masked based on the synthesized segmentation mask. The masked input medical image has an unmasked portion and a masked portion. An initial synthesized medical image is generated using a trained machine learning based generator network. The initial synthesized medical image includes a synthesized version of the unmasked portion of the masked input medical image and synthesized patterns in the masked portion of the masked input medical image. The synthesized patterns is fused with the input medical image to generate a final synthesized medical image.

Medical image synthesis of abnormality patterns associated with COVID-19

For machine learning for abnormality assessment in medical imaging and application of a machine-learned model, the machine learning uses regularization of the loss, such as regularization being used for training for abnormality classification in chest radiographs. The regularization may be a noise and/or correlation regularization directed to the noisy ground truth labels of the training data. The resulting machine-learned model may better classify abnormalities in medical images due to the use of the noise and/or correlation regularization in the training.

Machine learning from noisy labels for abnormality assessment in medical imaging

A computer-implemented method is for classifying a lesion. In an embodiment, the method includes receiving a first medical image of an examination volume, the first medical image corresponding to a first examination time; receiving a second medical image of the examination volume, the second medical image corresponding to a second examination time, different from the first examination time; determining a first lesion area corresponding to a lesion within the first medical image; determining a registration function based on a comparison of the first medical image and the second medical image; determining a second lesion area within the second medical image based on the registration function and the first lesion area; and classifying the lesion within the first medical image based on the second lesion area. A computer-implemented method for providing a trained classification function, a classification system, and computer program products and computer-readable media are also disclosed.

Classifying a lesion based on longitudinal studies

Systems and methods for identifying and assessing lymph nodes are provided. Medical image data (e.g., one or more computed tomography images) of a patient is received and anatomical landmarks in the medical image data are detected. Anatomical objects are segmented from the medical image data based on the one or more detected anatomical landmarks. Lymph nodes are identified in the medical image data based on the one or more detected anatomical landmarks and the one or more segmented anatomical objects. The identified lymph nodes may be assessed by segmenting the identified lymph nodes from the medical image data and quantifying the segmented lymph nodes. The identified lymph nodes and/or the assessment of the identified lymph nodes are output.

Hierarchical analysis of medical images for identifying and assessing lymph nodes

Growing community of inventors

Plainsboro, NJ, United States of America

Bogdan Georgescu