Siemens Healthcare Gmbh

Siemens Healthineers Ag

Siemens Aktiengesellschaft

Siemens Medical Solutions USA, Inc.

Other

Mehmet Akif Gulsun

Puneet Sharma

Yefeng Zheng

Tiziano Passerini

Bogdan Georgescu

Huseyin Tek

Vivek Kumar Singh

Saikiran Rapaka

Gareth Funka-Lea

Dominik Neumann

Mingqing Chen

Dorin Comaniciu

Shaohua Kevin Zhou

Shanhui Sun

Atilla Peter Kiraly

Marie-Pierre Jolly

Xiaoguang Lu

Zhoubing Xu

Michael Wels

Daguang Xu

Chris Schwemmer

Jin-hyeong Park

Max Schoebinger

Martin Berger

Tommaso Mansi

Andreas Greiser

Lucian Mihai Itu

Ankur Kapoor

Viorel Mihalef

Ingmar Voigt

Jens Guehring

Christoph Guetter

Arne Littmann

Shmuel Aharon

Edgar Müller

Martin Kraus

Serkan Cimen

Felix Lades

Max Schöbinger

Tae Soo Kim

Athira Jane Jacob

Hui Ding

Christian Hopfgartner

Diana Ioana Stoian

Alexandru Turcea

Rumman Khan

Suha Ayman

Ashish Jaiswal

Jonathan Sperl

Rainer Kaergel

Bruce S Spottiswoode

Bibo Shi

Michael Markl

Anamaria Vizitiu

Sebastien Piat

Costin Florian Ciusdel

Athira Jacob

Teodora Marina Chitiboi

Aurélien Stalder

Venkatesh Narasimha Murthy

Martin Von Roden

Sayan Ghosal

Rainer Kärgel

Patrick Magrath

Thibaut Barroyer

Pranjal Vaidya

Luis Carlos Garcia-Peraza Herrera

Florin-Cristian Ghesu

Angiography angles are determined. Patient information and target vessel information are obtained, wherein the patient information defines individual medical information of a patient and wherein the target vessel information defines at least one target vessel to be imaged. At least one angiography angle is determined based on the patient information and the target vessel information. Angiograms obtained using the at least one angiography angle are analyzed to determine a vessel coverage of the target vessel and based on the vessel coverage determines additional angiography angles.

Method and system for improved vessel coverage from angiography

For training for and performance of LGE analysis, multi-task machine-learning model is trained to output various cardiac tissue characteristics based on input of LGE MR data. The use of segmentation may be avoided or limited, resulting in a greater number of available training data samples, by using radiology clinical reports with LGE information as a source for samples. The multi-task model may be trained to output cardiac tissue characteristics using radiology clinical reports with LGE information with no segmentation or with segmentation for only a subset of the training samples. By training for multiple tasks, the accuracy of prediction for each task benefits from the information for other tasks. The trained model outputs values of characteristics for multiple tasks, such as extent of enhancement, type of enhancement, and localization of enhancement. Other tasks may be included, such as disease classification. Other inputs may be used, such as also including sensor data and/or cardiac motion.

Late gadolinium enhancement analysis for magnetic resonance imaging

At least one example embodiment provides a computer-implemented method for evaluating at least one image data set of an imaging region of a patient, wherein at least one evaluation information describing at least one medical condition in an anatomical structure of the imaging region is determined.

Computer-implemented methods and evaluation systems for evaluating at least one image data set of an imaging region of a patient, computer programs and electronically readable storage mediums

Systems and methods for graph based assessment of a patient are provided. Medical imaging data and non-imaging medical data of a patient are received. The medical imaging data and the non-imaging medical data are encoded into encoded features using a graph based machine learning network by comparing the patient with patients of a patient population based on a graph of the patient population. An assessment of the patient is determined based on the encoded features using a machine learning classifier network. The assessment of the patient is output.

Subpopulation based patient risk prediction using graph attention networks

Systems and methods for automatic assessment of a lesion are provided. One or more input medical images of a vessel of a patient is received. A lesion is defined in the one or more input medical images. A region of interest around the lesion is defined in the one or more input medical images. Radiomic features are extracted from the region of interest. An assessment of the lesion is determined using a machine learning based classifier network based on the radiomic features. The assessment of the lesion is output.

Fully automated assessment of coronary vulnerable plaque in coronary CT images using radiomic features

Anomalies in images are detected. A generative network and/or an autoencoder ('G/A-Network'), a Siamese network, a first training-dataset of normal images and a second training-dataset of abnormal images are provided. The G/A-network is trained to produce latent data from input images and output images from the latent data, wherein the training is performed with images of the first training-dataset, wherein a loss function is used for training at least at the beginning of training, and the loss function enhances the similarity of the input images and respective output images. The Siamese network is trained to generate similarity measures between input images and respective output images, wherein the training is performed with images of the first training-dataset and the second training-dataset in that images of both training-datasets are used as input images for the G/A-network and output images of the G/A-network are compared with their respective input images by the Siamese network.

System and method for detecting anomalies in images

Systems and methods for determining corresponding locations of points of interest in a plurality of input medical images are provided. A plurality of input medical images comprising a first input medical image and one or more additional input medical images is received. The first input medical image identifies a location of a point of interest. A set of features is extracted from each of the plurality of input medical images. Features between each of the sets of features are related using a machine learning based relational network. A location of the point of interest in each of the one or more additional input medical images that corresponds to the location of the point of interest in the first input medical image is identified based on the related features. The location of the point of interest in each of the one or more additional input medical images is output.

Multi-view matching across coronary angiogram images

Systems and methods for automated assessment of a vessel are provided. One or more input medical images of a vessel of a patient are received. A plurality of vessel assessment tasks for assessing the vessel is performed using a machine learning based model trained using multi-task learning. The plurality of vessel assessment tasks comprises segmentation of reference walls of the vessel from the one or more input medical images and segmentation of lumen of the vessel from the one or more input medical images. Results of the plurality of vessel assessment tasks are output.

Coronary lumen and reference wall segmentation for automatic assessment of coronary artery disease

One or more example embodiments of the present invention relates to a method for the automated determination of examination results in an image sequence from multiple chronologically consecutive frames, the method comprising determining diagnostic candidates in the form of contiguous image regions in the individual frames for a predefined diagnostic finding; and for a number of the diagnostic candidates, determining which candidate image regions in other frames correspond to the particular diagnostic candidate, determining whether the candidate image regions of the particular diagnostic candidate in the other frames overlap with other diagnostic candidates, generating a graph containing the determined diagnostic candidates of the frames as nodes and the determined overlaps as edges, and generating communities from nodes connected via edges.

Method and system for the automated determination of examination results in an image sequence

Systems and methods are provided for three dimensional depth reconstruction of vessels in two dimensional medical images. A medical image comprising braches of one or more vessels is received. A branch overlap image channel that represents a pixelwise probability that the branches overlap is generated. A set of branch orientation image channels are generated. Each branch orientation image channel is associated with one of a plurality of orientations. Each branch orientation image channel representing a image channel represents a pixelwise probability that the branches are oriented in its associated orientation. A multi-channel depth image is generated based on the branch overlap image channel and the set of branch orientation image channels. Each channel of the multi-channel depth image comprises portions of the branches corresponding to a respective depth.

3D depth reconstruction of vessels in 2D medical images

Systems and methods for automated assessment of a vessel are provided. One or more input medical images of a vessel of a patient are received. A plurality of vessel assessment tasks for assessing the vessel is performed using a machine learning based model trained using multi-task learning. The plurality of vessel assessment tasks are performed by the machine learning based model based on shared features extracted from the one or more input medical images. Results of the plurality of vessel assessment tasks or a combination of the results of the plurality of vessel assessment tasks are output.

Multi-task learning framework for fully automated assessment of coronary artery disease

Systems and methods for generating a segmentation mask of an anatomical structure, along with a measure of uncertainty of the segmentation mask, are provided. In accordance with one or more embodiments, a plurality of candidate segmentation masks of an anatomical structure is generated from an input medical image using one or more trained machine learning networks. A final segmentation mask of the anatomical structure is determined based on the plurality of candidate segmentation masks. A measure of uncertainty associated with the final segmentation mask is determined based on the plurality of candidate segmentation masks. The final segmentation mask and/or the measure of uncertainty are output.

Medical image segmentation with uncertainty estimation

Systems and methods for determining a 3D centerline of a vessel are provided. A current state observation of an artificial agent is determined based on one or more image view sets, each including 2D medical images of a vessel, a current position of the artificial agent in the 2D medical images, and a start position and a target position in the 2D medical images. Policy values are calculated for a plurality of actions for moving the artificial agent in 3D based on the current state observation using a trained machine learning model. The artificial agent is moved according to a particular action based on the policy values. The steps of determining, calculating, and moving are repeated for a plurality of iterations to move the artificial agent along a 3D path between the start position and the target position. The 3D centerline of the vessel is determined as the 3D path.

3D vessel centerline reconstruction from 2D medical images

Methods and systems for artificial intelligence based medical image segmentation are disclosed. In a method for autonomous artificial intelligence based medical image segmentation, a medical image of a patient is received. A current segmentation context is automatically determined based on the medical image and at least one segmentation algorithm is automatically selected from a plurality of segmentation algorithms based on the current segmentation context. A target anatomical structure is segmented in the medical image using the selected at least one segmentation algorithm.

Method and system for artificial intelligence based medical image segmentation

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Lawrenceville, NJ, United States of America

Mehmet Akif Gulsun