Siemens Healthcare Gmbh

Siemens Healthcare Diagnostics Gmbh

Siemens Aktiengesellschaft

Siemens Medical Solutions USA, Inc.

Siemens Healthineers Ag

Siemens Corporation

Noha Youssry El-Zehiry

Ali Kamen

Bogdan Georgescu

Saikiran Rapaka

Leo John Grady

Shaohua Kevin Zhou

Dorin Comaniciu

Shanhui Sun

David D Liu

Lance Anthony Ladic

Tommaso Mansi

Bernhard Geiger

Li Zhang

Gareth Funka-Lea

Oliver Hayden

Daguang Xu

Chris Schwemmer

Lukas Richter

Nathan S Lay

Matthias Ugele

Vincent Ordy

Philipp Wolber

Karim Amer

Atilla Peter Kiraly

Sasa Grbic

Michal Sofka

Oliver Schmidt

Ankur Kapoor

Rui Liao

Grzegorz Soza

Manfred Stanzel

Zhoubing Xu

Ingmar Voigt

Andreas Wimmer

Razvan Ionasec

Helene C Houle

Anton Schick

Jin-hyeong Park

Christian Tietjen

Michelle Xiaohong Yan

Young-Ho Kim

Gaby Marquardt

Sara Good

Daniela Seidel

Abdoul Aziz Amadou

Athira Jane Jacob

Sebastien Piat

Athira Jacob

Mihai Scutaru

Uwe Philippi

Anand Vinod Tatpati

Daniela Kühn

Mickael Sonni Albert Ibrahim Ide

Bo Liu

Jarrod Collins

Xianjun S Zheng

Systems and methods for automatically navigating a catheter in a patient are provided. An image of a current view of a catheter in a patient is received. A set of actions of a robotic navigation system for navigating the catheter from the current view towards a target view is determined using a machine learning based network. The catheter is automatically navigated in the patient from the current view towards the target view using the robotic navigation system based on the set of actions.

Smart image navigation for intracardiac echocardiography

A computer-implemented method for updating a boundary segmentation, the method including receiving image data and an original boundary segmentation including a plurality of boundary points. A plurality of edges in the image data is detected and used to generate an edge map. A confidence for a boundary point in the original boundary segmentation is computed where the confidence is based on a distance between the boundary point and an edge point associated with at least one of the plurality of edges of the edge map, and based on the confidence a classification of the boundary point is determined. An updated boundary segmentation based on the classification of the boundary point is generated and then output.

Updating boundary segmentations

The disclosure relates to a method for determining a boundary about an area of interest in an image set. The includes obtaining the image set from an imaging modality and processing the image set in a convolutional neural network. The convolutional neural network is trained to perform the acts of predicting an inverse distance map for the actual boundary in the image set; and deriving the boundary from the inverse distance map. The disclosure also relates to a method of training a convolutional neural network for use in such a method, and a medical imaging arrangement.

Method of computing a boundary

A system for performing adaptive focusing of a microscopy device comprises a microscopy device configured to acquire microscopy images depicting cells and one or more processors executing instructions for performing a method that includes extracting pixels from the microscopy images. Each set of pixels corresponds to an independent cell. The method further includes using a trained classifier to assign one of a plurality of image quality labels to each set of pixels indicating the degree to which the independent cell is in focus. If the image quality labels corresponding to the sets of pixels indicate that the cells are out of focus, a focal length adjustment for adjusting focus of the microscopy device is determined using a trained machine learning model. Then, executable instructions are sent to the microscopy device to perform the focal length adjustment.

Identifying the quality of the cell images acquired with digital holographic microscopy using convolutional neural networks

A cell visualization system includes a digital holographic microscopy (DHM) device, a training device, and a virtual staining device. The DHM device produces DHM images of cells and the virtual staining device colorizes the DHM images based on an algorithm generated by the training device using generative adversarial networks and unpaired training data. A computer-implemented method for producing a virtually stained DHM image includes acquiring an image conversion algorithm which was trained using the generative adversarial networks, receiving a DHM image with depictions of one or more cells and virtually staining the DHM image by processing the DHM image using the image conversion algorithm. The virtually stained DHM image includes digital colorization of the one or more cells to imitate the appearance of a corresponding actually stained cell.

Virtual staining of cells in digital holographic microscopy images using general adversarial networks

A computer-implemented method for analyzing digital holographic microscopy (DHM) data for hematology applications includes receiving a DHM image acquired using a digital holographic microscopy system. The DHM image comprises depictions of one or more cell objects and background. A reference image is generated based on the DHM image. This reference image may then be used to reconstruct a fringe pattern in the DHM image into an optical depth map.

Auto-referencing in digital holographic microscopy reconstruction

The present invention relates to an improved method for marker-free detection of a cell type of at least one cell in a medium using microfluidics and digital holographic microscopy, as well as a device, particular for carrying out the method.

High accuracy 5-part differential with digital holographic microscopy and untouched leukocytes from peripheral blood

A computer-implemented method for deriving biopsy results in a non-invasive manner includes acquiring a plurality of training data items. Each training data item comprises non-invasive patient data and one or more biopsy derived scores associated with an individual. The method further includes extracting a plurality of features from the non-invasive patient data based on the one or more biopsy derived scores and training a predictive model to generate a predicted biopsy score based on the plurality of features and the one or more biopsy derived scores.

Virtual biopsy techniques for analyzing diseases

A method for analyzing digital holographic microscopy (DHM) data for hematology applications includes receiving a DHM image acquired using a digital holographic microscopy system and identifying one or more erythrocytes in the DHM image. For each respective erythrocyte included in the one or more erythrocytes, a cell thickness value for the respective erythrocyte using a parametric model is estimated, and a cell volume value is calculated for the respective erythrocyte using the cell thickness value.

Analyzing digital holographic microscopy data for hematology applications

A method for analyzing digital holographic microscopy (DHM) data for hematology applications includes receiving a plurality of DHM images acquired using a digital holographic microscopy system. One or more connected components are identified in each of the plurality of DHM images and one or more training white blood cell images are generated from the one or more connected components. A classifier is trained to identify a plurality of white blood cell types using the one or more training white blood cell images. The classifier may be applied to a new white blood cell image to determine a plurality of probability values, each respective probability value corresponding to one of the plurality of white blood cell types. The new white blood cell image and the plurality of probability values may then be presented in a graphical user interface.

A method for predicting a cancer staging score from medical image data includes receiving patient data for a plurality of patients, where patient data for each of the plurality of patients includes one or more of an image volume of a suspected tumor in an organ, blood test data, demographic data, and ground truth tumor staging scores for the suspected tumor in the organ, extracting features from the patient data, and using the features extracted from the patient data to train a classifier to predict a cancer staging score for a new patient from one or more of an image volume of a suspected tumor in the organ, patient blood test data and patient demographic data of that new patient.

Biopsy-free detection and staging of cancer using a virtual staging score

Anatomy, such as papillary muscle, is automatically detected () and/or detected in real-time. For automatic detection () of small anatomy, machine-learnt classification with spatial () and temporal (e.g., Markov) () constraints is used. For real-time detection, sparse machine-learnt detection () interleaved with optical flow tracking () is used.

Landmark detection with spatial and temporal constraints in medical imaging

Growing community of inventors

Plainsboro, NJ, United States of America

Noha Youssry El-Zehiry