Siemens Healthcare Gmbh

Rainer Kaergel

Michael Suehling

Jonathan Sperl

Alexander Muehlberg

Bogdan Georgescu

Puneet Sharma

Saikiran Rapaka

Mehmet Akif Gulsun

Michael Wels

Dominik Neumann

Chris Schwemmer

Max Schoebinger

Lutz Guendel

Alexander Katzmann

Xinyun Li

Sven Bauer

A computer-implemented method for comparing follow-up medical findings includes receiving image data showing a body part of a patient at a first time and retrieving, from a database, reference data associated to the image data. The reference data includes reference biometric data and one or more reference medical findings. Thereby the reference biometric data and the one or more reference medical findings have been extracted from reference image data depicting the body part of the patient at a second time, different than the first time. The method further includes processing the received image data to extract, from the image data, biometric data corresponding to the reference biometric data and one or more medical findings; performing a registration of the extracted biometric data with the reference biometric data; and comparing the extracted medical findings with the reference medical findings using the registration.

Patient follow-up analysis

A method and device are for automatic determination of the change of a hollow organ. The method includes providing a first medical image of the organ recorded at a first time; computing a first representation of the organ in the first image; computing a first reference-line of the organ based on the first representation and providing a second medical image of the organ recorded at a second point. The method further includes computing a second representation of the organ in the second image; computing a second reference-line of the organ based on the second representation of the organ; registering of the first and second reference-line to obtain at least one of matched representations of the organ and features derived from the matched representations of the organs; and comparing at least one of the matched representations of the organs and the features derived from the matched representations of the organ.

Method and device for automatic determination of the change of a hollow organ

A method is for obtaining at least one feature of interest, especially a biomarker, from an input image acquired by a medical imaging device. The at least one feature of interest is the output of a respective node of a machine learning network, in particular a deep learning network. The machine learning network processes at least part of the input image as input data. The used machine learning network is trained by machine learning using at least one constraint for the output of at least one inner node of the machine learning network during the machine learning.

Method for obtaining at least one feature of interest

Systems and methods are provided for evaluating an aorta of a patient. A medical image of an aorta of a patient is received. The aorta is segmented from the medical image. One or more measurement planes are identified on the segmented aorta. At least one measurement is calculated at each of the one or more measurement planes. The aorta of the patient is evaluated based on the at least one measurement calculated at each of the one or more measurement planes.

Automatic detection and quantification of the aorta from medical images

A method, for two-dimensional mapping of anatomical structures of a patient, includes acquiring three-dimensional image data of anatomical structures of a patient; adapting a virtual network structure to a spatial course of the anatomical structures; defining a user-defined map projection for projection of two-dimensional pixel positions of an image to be output onto a geometric figure around a center of the anatomical structures for which mapping onto a two-dimensional space is defined; ascertaining points of intersection of radially extending half lines assigned to the two-dimensional pixel positions of the image to be output with the virtual network structure; and ascertaining the image to be output based upon image intensity values assigned to the points of intersection ascertained. A method for two-dimensional mapping of the tree-like elongated structure of the patient; a method for simultaneous mapping of a tree-like elongated structure; and corresponding apparatuses are also described.

Planar visualization of anatomical structures

A method is for determining at least one object feature of an object at least partially depicted by an object image. A respective preliminary feature for each object feature and at least one acquisition feature are determined from the object image. The preliminary feature depends on the object feature and on an imaging device used to acquire the object image and/or at least one imaging parameter used for acquiring and/or reconstructing the object image and/or an additional feature of the object. The acquisition feature depends on the imaging device and/or the imaging parameter and/or the additional feature of the object. A correction algorithm determines the object feature from the preliminary feature and the acquisition feature. The acquisition feature is determined by a respective determination algorithm that is selected and/or parametrized from a group of candidate algorithms, depending on multiple reference images.

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Forchheim, Germany

Rainer Kaergel