Siemens Healthcare Gmbh

Other

Siemens Healthineers Ag

Daniel Toth

Peter Mountney

Tanja Kurzendorfer

Kawal Rhode

Tommaso Mansi

Pascal Ceccaldi

Alexander Brost

Maria Panayiotou

Rui Liao

Shun P Miao

Serkan Cimen

Sebastien Piat

Christopher A Rinaldi

Jonathan Behar

Aldo Rinaldi

Sabrina Reiml

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

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 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

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Twickenham, United Kingdom

Daniel Toth