Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

Siemens Medical Solutions USA, Inc.

Siemens Corporation

Siemens Energy, Inc.

Siemens Product Lifecycle Management Software Inc.

Johns Hopkins University

Siemens Aktiengesell Schaft

Z2sky Technologies Inc.

Siemens Healthineers Ag

Siemens Industry Software Gmbh

Shaohua Kevin Zhou

Dorin Comaniciu

David D Liu

Bogdan Georgescu

Yefeng Zheng

Jingdan Zhang

Daguang Xu

Zhoubing Xu

Jin-hyeong Park

Mingqing Chen

Michal Sofka

Dijia Wu

Tommaso Mansi

Nathan S Lay

Atilla Peter Kiraly

Grzegorz Soza

Neil Birkbeck

Hien Nguyen

Bernhard Geiger

Michael Suehling

Christian Tietjen

Waheed A Abbasi

Puneet Sharma

Sasa Grbic

Klaus Engel

Michael Kelm

Dominik Neumann

Wen Hong Liu

Florin Cristian Ghesu

Jan Kretschmer

Vivek Kumar Singh

Berthold Kiefer

Noha Youssry El-Zehiry

Constantine Simopoulos

El Mahjoub Rasselkorde

Terrence Chen

Shanhui Sun

Benjamin L Odry

Li Zhang

John I Jackson

Gareth Funka-Lea

Xiaoguang Lu

Michael J Metala

Adrian Barbu

Andreas Wimmer

Dominik Bernhardt

Jan Boese

Mariappan S Nadar

Xiang Sean Zhou

Rui Liao

Matthias John

Shun P Miao

Daphne Yu

Mehmet Akif Gulsun

Michael Wels

Razvan Ioan Ionasec

Fernando Vega-Higuera

Chris Schwemmer

Peng Wang

Olivier Pauly

Andre De Oliveira

Martin Huber

Siqi Liu

Christopher Vincent Alvino

Gunnar Krüger

Peter Gall

Leo John Grady

Boris Mailhe

Rainer Graumann

Thomas Mertelmeier

Yao-Jen Chang

Lucian Mihai Itu

Carol L Novak

Wei Zhang

Gerhard Kleinszig

Viorel Mihalef

Anna Jerebko

Xiao Chen

Gianluca Paladini

Norbert Strobel

Kai Ma

Ingmar Voigt

Wen Wu

Qiu Wang

Mark William Fischer

Nikolai R Tevs

Martin Ostermeier

Robert Grimm

Jens Guehring

Martin Requardt

Jerome Declerck

Hasan Ertan Cetingul

Andreas Krauss

Yang Wang

Julian Krebs

Clifford R Weiss

Nancy H Ulerich

Jens Wetzl

Martin Kramer

Thomas Friese

For three-dimensional segmentation from two-dimensional intracardiac echocardiography imaging, the three-dimension segmentation is output by a machine-learnt multi-task generator. Rather than the brute force approach of training the generator from 2D ICE images to output a 2D segmentation, the generator is trained from 3D information, such as a sparse ICE volume assembled from the 2D ICE images. Where sufficient ground truth data is not available, computed tomography or magnetic resonance data may be used as the ground truth for the sample sparse ICE volumes. The generator is trained to output both the 3D segmentation and a complete volume (i.e., more voxels represented than in the sparse ICE volume). The 3D segmentation may be further used to project to 2D as an input with an ICE image to another network trained to output a 2D segmentation for the ICE image. Display of the 3D segmentation and/or 2D segmentation may guide ablation of tissue in the patient.

Three-dimensional segmentation from two-dimensional intracardiac echocardiography imaging

For three-dimensional segmentation from two-dimensional intracardiac echocardiography imaging, the three-dimension segmentation is output by a machine-learnt multi-task generator. The machine-learnt multi-task generator is trained from 3D information, such as a sparse ICE volume assembled from the 2D ICE images. The machine-learnt multi-task generator is trained to output both the 3D segmentation and a complete volume. The 3D segmentation may be used to project to 2D as an input with an ICE image to another network trained to output a 2D segmentation for the ICE image. Display of the 3D segmentation and/or 2D segmentation may guide ablation of tissue in the patient.

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

Systems and methods are provided for performing medical imaging analysis. Input medical imaging data is received for performing a particular one of a plurality of medical imaging analyses. An output that provides a result of the particular medical imaging analysis on the input medical imaging data is generated using a neural network trained to perform the plurality of medical imaging analyses. The neural network is trained by learning one or more weights associated with the particular medical imaging analysis using one or more weights associated with a different one of the plurality of medical imaging analyses. The generated output is outputted for performing the particular medical imaging analysis.

Hierarchical learning of weights of a neural network for performing multiple analyses

An anatomical structure is detected () in a volume of ultrasound data by identifying () the anatomical structure in another volume of ultrasound data and generating () an image of the anatomical structure and an anatomical landmark. A group of images are generated () of the original volume and compared () to the image of the other volume. An image of the group of images is selected () as including the anatomical structure based on the comparison.

Intervolume lesion detection and image preparation

A method of visualizing spinal nerves includes receiving a 3D image volume depicting a spinal cord and a plurality of spinal nerves. For each spinal nerve, a 2D spinal nerve image is generated by defining a surface within the 3D volume comprising the spinal nerve. The surface is curved such that it passes through the spinal cord while encompassing the spinal nerve. Then, the 2D spinal nerve images are generated based on voxels on the surface included in the 3D volume. A visualization of the 2D spinal images is presented in a graphical user interface that allows each 2D spinal image to be viewed simultaneously.

System and method for next-generation MRI spine evaluation

Systems and methods are provided for automatic detection and quantification for traumatic bleeding. Image data is acquired using a full body dual energy CT scanner. A machine-learned network detects one or more bleeding areas on a bleeding map from the dual energy CT scan image data. A visualization is generated from the bleeding map. The predicted bleeding areas are quantified, and a risk value is generated. The visualization and risk value are presented to an operator.

Detection and quantification for traumatic bleeding using dual energy computed tomography

Intelligent multi-scale image parsing determines the optimal size of each observation by an artificial agent at a given point in time while searching for the anatomical landmark. The artificial agent begins searching image data with a coarse field-of-view and iteratively decreases the field-of-view to locate the anatomical landmark. After searching at a coarse field-of view, the artificial agent increases resolution to a finer field-of-view to analyze context and appearance factors to converge on the anatomical landmark. The artificial agent determines applicable context and appearance factors at each effective scale.

Intelligent multi-scale medical image landmark detection

An apparatus and method for coupled medical image formation and medical image signal recovery using a dual domain network is disclosed. The dual-domain network includes a first deep neural network (DNN) to perform signal recovery in a sensor signal domain and a second DNN to perform signal recovery in an image domain. A sensor signal is acquired by a sensor of a medical imaging device. A refined sensor signal is generated from the received sensor signal using the first DNN. A first reconstructed medical image is generated from the received sensor signal. A second reconstructed medical image is generated from the refined sensor signal generated by the first DNN. An enhanced medical image is generated based on the both the first reconstructed medical image and the second reconstructed medical image using the second DNN. The enhanced medical image generated by the second DNN is displayed.

Unified dual-domain network for medical image formation, recovery, and analysis

Methods and apparatus for automated medical image analysis using deep learning networks are disclosed. In a method of automatically performing a medical image analysis task on a medical image of a patient, a medical image of a patient is received. The medical image is input to a trained deep neural network. An output model that provides a result of a target medical image analysis task on the input medical image is automatically estimated using the trained deep neural network. The trained deep neural network is trained in one of a discriminative adversarial network or a deep image-to-image dual inverse network.

Adversarial and dual inverse deep learning networks for medical image analysis

Medical image data may be applied to a machine-learned network learned on training image data and associated image segmentations, landmarks, and view classifications to classify a view of the medical image data, detect a location of one or more landmarks in the medical image data, and segment a region in the medical image data based on the application of the medical image data to the machine-learned network. The classified view, the segmented region, or the location of the one or more landmarks may be output.

Segmentation, landmark detection and view classification using multi-task learning

A method, apparatus and non-transitory computer readable medium are for segmenting different types of structures, including cancerous lesions and regular structures like vessels and skin, in a digital breast tomosynthesis (DBT) volume. In an embodiment, the method includes: pre-classification of the DBT volume in dense and fatty tissue and based on the result; localizing a set of structures in the DBT volume by using a multi-stream deep convolutional neural network; and segmenting the localized structures by calculating a probability for belonging to a specific type of structure for each voxel in the DBT volume by using a deep convolutional neural network for providing a three-dimensional probabilistic map.

Semantic segmentation for cancer detection in digital breast tomosynthesis

Tissue is characterized using machine-learnt classification. The prognosis, diagnosis or evidence in the form of a similar case is found by machine-learnt classification from features extracted from frames of medical scan data. The texture features for tissue characterization may be learned using deep learning. Using the features, therapy response is predicted from magnetic resonance functional measures before and after treatment in one example. Using the machine-learnt classification, the number of measures after treatment may be reduced as compared to RECIST for predicting the outcome of the treatment, allowing earlier termination or alteration of the therapy.

Tissue characterization based on machine learning in medical imaging

Growing community of inventors

Plainsboro, NJ, United States of America

Shaohua Kevin Zhou