Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

Other

Siemens Healthineers Ag

Peter Mountney

Daniel Toth

Dorin Comaniciu

Jan Boese

Matthias John

Sasa Grbic

Philip Mewes

Razvan Ionasec

Pascal Ceccaldi

Tanja Kurzendorfer

Kawal Rhode

Tommaso Mansi

Razvan Ioan Ionasec

Alexander Brost

Sebastien Piat

Serkan Cimen

Maria Panayiotou

Ankur Kapoor

Rui Liao

Thomas Engel

Shun P Miao

Ingmar Voigt

Thomas Pheiffer

Andreas Maier

Parmeshwar Khurd

Maximilian Allan

Simone Severini

Markus Kaiser

Sabrina Reiml

Aldo Rinaldi

Jonathan Behar

Christopher A Rinaldi

Mark Herbster

Manish Sahu

Method and system for image registration or image segmentation. The method includes receiving an image which is to be processed by a first machine-learning model to perform, for example, image registration or segmentation, and using a second machine-learning model to determine if the received image is of a quality suitable for the first machine-learning model to act upon.

Method and system for image analysis

In a system and method for analyzing images, an input image is provided to a computer and is processed therein with a first deep learning model so as to generate an output result for the input image; and applying a second deep learning model is applied to the input image to generate an output confidence score that is indicative of the reliability of any output result from the first deep learning model for the input image.

Method and system for generating a confidence score using deep learning model

In a method and apparatus for training a computer system for use in classification of an image by processing image data representing the image, image data are compressed and then loaded into a programmable quantum annealing device that includes a Restricted Boltzmann Machine. The Restricted Boltzmann Machine is trained to act as a classifier of image data, thereby providing a trained Restricted Boltzmann Machine; and, the trained Restricted Boltzmann Machine is used to initialize a neural network for image classification thereby providing a trained computer system for use in classification of an image.

Data encoding and classification

A method is for controlling a system including an imaging modality. The method includes receiving an image and/or a series of images of a portion of a patient from the imaging modality, the field of view of the image and/or of the series of images covering at least a part of an instrument; determining whether the part of the instrument is located within a defined portion of the field of view and/or in a given state selected from a plurality of first states and/or the part of the instrument executes a given movement selected from a plurality of movements; and generating a control command for performing an action depending on whether the part of the instrument is located within the defined portion of the field of view and/or the part of the instrument is in the given state and/or the part of the instrument executes the given movement.

Device and method for controlling a system comprising an imaging modality

The disclosure relates to a method of determining a transformation between coordinate frames of sets of image data. The method includes receiving a model of a structure extracted from first source image data, the first source image data being generated according to a first imaging modality and having a first data format, wherein the model has a second data format, different from the first data format. The method also includes determining, using an intelligent agent, a transformation between coordinate frames of the model and first target image data, the first target image data being generated according to a second imaging modality different to the first imaging modality.

Determining a transformation between coordinate frames of sets of image data

A method of training a computer system for use in determining a transformation between coordinate frames of image data representing an imaged subject. The method trains a learning agent according to a machine learning algorithm, to determine a transformation between respective coordinate frames of a number of different views of an anatomical structure simulated using a 3D model. The views are images containing labels. The learning agent includes a domain classifier comprising a feature map generated by the learning agent during the training operation. The classifier is configured to generate a classification output indicating whether image data is synthesized or real images data. Training includes using unlabeled real image data to training the computer system to determine a transformation between a coordinate frame of a synthesized view of the imaged structure and a view of the structure within a real image. This is done whilst deliberately reducing the ability of the domain classifier to discriminate between a synthesized image and a real image of the structure.

Method, learning apparatus, and medical imaging apparatus for registration of images

A method of extracting mechanical activation of the left ventricle from a sequence of contrasted X-ray fluoroscopy images is provided. The method includes: processing the image sequence to perform segmentation of the coronary veins; annotating branches of the segmented coronary veins; tracking the positions of the annotations throughout the fluoroscopy image sequence; computing the principle components of the motion of the annotations from the tracked positions; projecting the motion of the annotations to the axis corresponding to a first principle component; and analyzing the resulting motion curves to identify a latest activating region of the left ventricle.

Method of processing fluoroscopy images

A method for registering image data sets of a target region of a patient includes selecting a first anatomical structure only or at least partially only visible in the first image data set, and a second anatomical structure only or at least partially only visible in the second image data set, such that there is a known geometrical relationship between extended segments of the anatomical structures; automatically determining a first geometry information describing the geometry of at least a part of the first anatomical structure and a second geometry information describing the geometry of at least a part of the second anatomical structure, neither information being sufficient to enable registration of the image data sets on its own; automatically optimizing transformation parameters describing a rigid transformation of one of the anatomical structures with respect to the other and geometrical correspondences; and determining registration information from the optimized transformation parameters.

Methods and devices for registration of image data sets of a target region of a patient

In order to improve depth perception for an image displayed during a laparoscopic surgery, a representation of a shadow of a tool included in the image and used in the laparoscopic surgery is identified and introduced into the image. A processor augments a three-dimensional (3D) model including a 3D representation of a surface of an object included in the image, and a representation of the tool by introducing a virtual light source into the 3D model to generate a virtual shadow within the 3D model. The processor subtracts the representation of the shadow out of the augmented 3D model and superimposes the representation of the shadow on the image to be displayed during the laparoscopic surgery.

Virtual shadows for enhanced depth perception

In a method for visualization of scar tissue in medical imaging data of a heart, medical imaging data representing a heart myocardium and scar tissue within the heart myocardium are obtained and provided to a computer. The computer deviates the thickness of the myocardium into a number of layers and calculates the presence and distribution of scar tissue within each of the layers. The scar tissue is shown in a visualization of the myocardium; and a user is provided with controls to allow the user to select which of the layers of scar tissue is visualised.

Methods for visualizing heart scar tissue

A method and system for registering ultrasound images and physiological models to x-ray fluoroscopy images is disclosed. A fluoroscopic image and an ultrasound image, such as a Transesophageal Echocardiography (TEE) image, are received. A 2D location of an ultrasound probe is detected in the fluoroscopic image. A 3D pose of the ultrasound probe is estimated based on the detected 2D location of the ultrasound probe in the fluoroscopic image. The ultrasound image is mapped to a 3D coordinate system of a fluoroscopic image acquisition device used to acquire the fluoroscopic image based on the estimated 3D pose of the ultrasound probe. The ultrasound image can then be projected into the fluoroscopic image using a projection matrix associated with the fluoroscopic image. A patient specific physiological model can be detected in the ultrasound image and projected into the fluoroscopic image.

Method and system for registration of ultrasound and physiological models to X-ray fluoroscopic images

A method for real-time fusion of a 2D cardiac ultrasound image with a 2D cardiac fluoroscopic image includes acquiring real time synchronized US and fluoroscopic images, detecting a surface contour of an aortic valve in the 2D cardiac ultrasound (US) image relative to an US probe, detecting a pose of the US probe in the 2D cardiac fluoroscopic image, and using pose parameters of the US probe to transform the surface contour of the aortic valve from the 2D cardiac US image to the 2D cardiac fluoroscopic image.

Image fusion for interventional guidance

Methods, systems, and apparatus for obtaining a sequence of x-ray images are disclosed. An object of interest in a first x-ray image is detected and an area of interest, based on a predicted motion of the object of interest, is determined. A second x-ray image of the area of interest is acquired using spatial x-ray modification to control an x-ray to pass through a portion of a patient corresponding to the area of interest.

Method and system for obtaining a sequence of x-ray images using a reduced dose of ionizing radiation

A method and system for model-based fusion of pre-operative image data and intra-operative fluoroscopic images is disclosed. A fluoroscopic image and an ultrasound image are received. The ultrasound image is mapped to a 3D coordinate system of a fluoroscopic image acquisition device used to acquire the fluoroscopic image. Contours of an anatomical structure are detected in the ultrasound image, and a transformation is calculated between the ultrasound image and a pre-operative CT image based on the contours and a patient-specific physiological model extracted from the pre-operative CT image. A final mapping is determined between the CT image and the fluoroscopic image based on the transformation between the ultrasound image and physiological model and the mapping of the ultrasound image to the 3D coordinate system of the fluoroscopic image acquisition device. The CT image or the physiological model can then be projected into the fluoroscopic image.

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London, United Kingdom

Peter Mountney