Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

Siemens Medical Solutions USA, Inc.

Siemens Corporation

Siemens Aktiengesell Schaft

Siemens Healthineers Ag

Yefeng Zheng

Dorin Comaniciu

Bogdan Georgescu

Shaohua Kevin Zhou

Jan Boese

Puneet Sharma

Gareth Funka-Lea

Matthias John

Vivek Kumar Singh

Hien Nguyen

David D Liu

Mehmet Akif Gulsun

Frank Sauer

Fernando Vega-Higuera

Tommaso Mansi

Michael Kelm

Daguang Xu

Razvan Ioan Ionasec

Mingqing Chen

Ali Kamen

Michael Scheuering

Xiaoguang Lu

Rui Liao

Shanhui Sun

Sasa Grbic

Saikiran Rapaka

Viorel Mihalef

Zhoubing Xu

Adrian Barbu

Ingmar Voigt

Dominik Neumann

Wen Hong Liu

Florin Cristian Ghesu

Haofu Liao

S Kevin Zhou

Yucheng Tang

Atilla Peter Kiraly

Li Zhang

Günter Lauritsch

Christopher Rohkohl

Jingdan Zhang

Jin-hyeong Park

Peng Wang

Arne Littmann

Yang Wang

Alois Nöttling

Alexey Tsymbal

Sushil Mittal

Tae Soo Kim

Kerstin Mueller

Hui Ding

Yu Pang

Felix Lugauer

Alexander Schwing

Mariappan S Nadar

Boris Mailhe

Xiang Sean Zhou

Benjamin L Odry

Tiziano Passerini

Michael Suehling

Shun P Miao

Edgar Mueller

Xiao Chen

Michael Wels

Qiu Wang

Razvan Ionasec

Martin Harder

Dominik Bernhardt

Klaus Klingenbeck

Huseyin Tek

Julian Krebs

Martin Huber

Jan Kretschmer

Edgar Müller

Nathan S Lay

Hua Zhong

Ahmet Tuysuzoglu

Dime Vitanovski

Maria Jimena Costa

Ying Chi

Guenter Lauritsch

Joanne Otsuki

Xiaojie Huang

Gustavo Henrique Monteiro De Barros Carneiro

Nora Hünemohr

Shiyang Lu

Lei Zhang

Pavlo Dyban

Xi Cheng

Stratis Tzoumas

Zizhao Zhang

Michael Lynch

Jianhua Shen

Tianzhou Wang

Uwe Kirschstein

Haibin Ling

Jing Yang

Dominik Zaeuner

Pengyue Zhang

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

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

Systems and methods for generating synthesized images are provided. An input medical image of a patient in a first domain is received. A synthesized image in a second domain is generated from the input medical image of the patient in the first domain using a first generator. The first generator is trained based on a comparison between segmentation results of a training image in the first domain from a first segmentor and segmentation results of a synthesized training image in the second domain from a second segmentor. The synthesized training image in the second domain is generated by the first generator from the training image in the first domain. The synthesized image in the second domain is output.

Synthesizing and segmenting cross-domain medical images

A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Method and system for machine learning based assessment of fractional flow reserve

A method and apparatus for automatically performing medical image analysis tasks using deep image-to-image network (DI2IN) learning. An input medical image of a patient is received. An output image that provides a result of a target medical image analysis task on the input medical image is automatically generated using a trained deep image-to-image network (DI2IN). The trained DI2IN uses a conditional random field (CRF) energy function to estimate the output image based on the input medical image and uses a trained deep learning network to model unary and pairwise terms of the CRF energy function. The DI2IN may be trained on an image with multiple resolutions. The input image may be split into multiple parts and a separate DI2IN may be trained for each part. Furthermore, the multi-scale and multi-part schemes can be combined to train a multi-scale multi-part DI2IN.

Deep image-to-image network learning for medical image analysis

Systems and methods are provided for automatic segmentation of a vessel. A sequence of image slices containing a vessel is acquired. Features maps are generated for each of the image slices using a trained fully convolutional neural network. A trained bi-directional recurrent neural network generates a segmented image based on the feature maps.

Vascular segmentation using fully convolutional and recurrent neural networks

A network is machine trained to estimate flow by spatial location based on input of anatomy information. A medical scan of tissue may be used to generate flow information without the delay or difficulty of performing a medical scan configured for flow imaging or CFD. Anatomy imaging is used to provide flow estimates with the speed provided by the machine-learned network.

Tissue-to-flow image generation in medical imaging

A method and system for virtual percutaneous valve implantation is disclosed. A patient-specific anatomical model of a heart valve is estimated based on 3D cardiac medical image data and an implant model representing a valve implant is virtually deployed into the patient-specific anatomical model of the heart valve. A library of implant models, each modeling geometrical properties of a corresponding valve implant, is maintained. The implant models maintained in the library are virtually deployed into the patient specific anatomical model of the heart valve to select an implant type and size and deployment location and orientation for percutaneous valve implantation.

Method and system for virtual percutaneous valve implantation

A learning-based magnetic resonance fingerprinting (MRF) reconstruction method for reconstructing an MR image of a tissue space in an MR scan subject for a particular MR sequence is disclosed. The method involves using a machine-learning algorithm that has been trained to generate a set of tissue parameters from acquired MR signal evolution without using a dictionary or dictionary matching.

System and method for learning based magnetic resonance fingerprinting

Embodiments can provide a method for atlas-based contouring, comprising constructing a relevant atlas database; selecting one or more optimal atlases from the relevant atlas database; propagating one or more atlases; fusing the one or more atlases; and assessing the quality of one or more propagated contours.

Atlas-based contouring of organs at risk for radiation therapy

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Dayton, NJ, United States of America

Yefeng Zheng