Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

Fraunhofer-Gesellschaft Zur Foerderung Der Angewandten Forschung E.V.

Siemens Healthineers Ag

Christian Tietjen

Shaohua Kevin Zhou

David D Liu

Grzegorz Soza

Sven Kohle

Dorin Comaniciu

Gerardo Hermosillo Valadez

Dijia Wu

Jan Kretschmer

Felix Ritter

Michael Suehling

Rainer Grimmer

Johannes Baeck

Marcus Thaele

Felix Nensa

Steffen Weichert

Leo John Grady

Rainer Raupach

Michal Sofka

Thomas Beck

Thomas Allmendinger

Andreas Wimmer

Noha Youssry El-Zehiry

Ute Feuerlein

Matthias Wolf

Martin Kramer

Michael Sühling

Shu Liao

Robert Lapp

Maria Jimena Costa

Michael Rusitska

Christiane Koch

Simon Allen-Raffl

Stefan Kaepplinger

Saulius Archipovas

Matthias Baer

Silvie Grossmann

Mathias Neugebauer

Anja Schnaars

Hao Xu

Paul Swoboda

Marcus Thäle

A computer-implemented method for identifying pathological changes in follow-up medical images is provided. In an embodiment, the method includes: providing reference image data showing a body part of a patient at a first time; providing follow-up image data showing a body part of a patient at a subsequent second time; generating one or more deformation fields for the reference image data and the follow-up image data describing anatomical deformations in the body part between the reference image data and the follow-up image data using at least one image registration; aligning the reference image data and the follow up image data using the one or more deformation fields to generate co-aligned image data; analyzing the co-aligned image data to identify pathological changes in the body part from the reference image data to the follow image data using a machine learned network trained to recognize pathological relevant changes in co-aligned image data.

Method and system for identifying pathological changes in follow-up medical images

A system is used to control operation of a user device. An association between a medical condition and one or more decision trees is maintained. Each decision tree includes nodes organised in a tree structure originating at a root node and terminating at leaf nodes via branch nodes. The nodes are linked to each other via outputs and each output causes the system to differently control operation of the user device. Responsive to receiving medical information associated with the patient, using the association, a decision tree is identified. For the root node of the identified decision tree: a first node is selected, responsive to receiving data indicative of a first output regarding the root node; the user device is caused to retrieve medical information from one or more data sources based on the first node; and the retrieved medical information is caused to be displayed on the user device.

Healthcare network

In one embodiment, a computer-implemented method is for providing lymph node information. The method includes receiving medical imaging data; receiving atlas data spatially relating lymph node stations to at least one non-lymphatic anatomical structure; determining a lymph node position in the medical imaging data; generating the lymph node information, the lymph node information being indicative of a lymph node station, to which the lymph node position is anatomically allocated, by applying an algorithm onto the medical imaging data, the atlas data and the lymph node position; and providing the lymph node information.

Method and data processing system for providing lymph node information

A method and system are for identification of at least one medical reference image. An embodiment of the method includes providing a medical representation image based on a current examination image depicting a body part of a first patient; defining a region of interest in the medical representation image; generating a feature signature, at least for the region of interest; comparing the medical representation image with a plurality of medical images of at least one second patient stored in a medical image database, based on the feature signature generated; and identifying at least one medical image in the medical image database as the at least one medical reference image, the at least one medical reference image providing a similarity degree to the medical representation image above a threshold. In an embodiment, the generating is performed using a trained machine-learning algorithm.

Searching a medical reference image

A system is for supporting evaluation of a medical image data set. The system includes an interface unit to provide a display of a medical image data set, visualize an evaluation tool, visualize at least one result of an evaluation function, and receive user input relating to navigating the evaluation tool in the medical image data set to a region of interest, and selecting at least one of the evaluation functions. The system further includes a computing unit to process the user input to navigate the evaluation tool, perform the at least one evaluation function, and generate a visualization of at least one result of the evaluation function. The at least one result is adapted to be visualized as an overlay to the displayed image data set and/or evaluation tool.

Integrated medical image visualization and exploration

The invention relates to a method for imaging a three-dimensional object to be examined. According to said method, a three-dimensional parameterized area is determined which is in conformity with an anatomic structure of the three-dimensional object to be examined. The three-dimensional parameterized area is imaged onto a two-dimensional parameterized area. The three-dimensional object to be examined is represented by imaging pixels that are associated with the three-dimensional parameterized area onto the two-dimensional parameterized area. The invention further relates to a method for determining a camera position in a three-dimensional image recording of an object to be examined. The invention also relates to a method for representing a section of an object to be examined. The invention finally relates to a device for imaging a three-dimensional object to be examined.

Reformatting while taking the anatomy of an object to be examined into consideration

An embodiment of the invention relates to a scanning device. The scanning device includes a scanning unit to detect radiation received during a scanning operation on an object. An imaging unit is arranged to reconstruct an image for a location on the object based on the detected radiation. A texture analysis unit receives an indicated area of interest of a medical image and computes at least one texture metric for the area of interest. An image comparison unit receives a plurality of texture metrics for a common area of interest within respective medical images and outputs a change metric indicating a measure of variation over time for the area of interest based on a comparison of the plurality of texture metrics.

Comparing medical images

A method is for checking a medical image with regard to a processing of the medical image via an image processing module. The method includes providing at least one input requirement of the image processing module relating to at least one image parameter of the medical image; providing the at least one image parameter of the medical image; and checking whether the at least one image parameter fulfills the at least one input requirement.

Method for checking a medical image and method for adapting an examination protocol during a medical imaging examination

For therapy response assessment, texture features are input for machine learning a classifier and for using a machine learnt classifier. Rather than or in addition to using formula-based texture features, data driven texture features are derived from training images. Such data driven texture features are independent analysis features, such as features from independent subspace analysis. The texture features may be used to predict the outcome of therapy based on a few number of or even one scan of the patient.

Early therapy response assessment of lesions

A method is disclosed for determining a boundary surface network of the tubular object. In an embodiment, a representation of the tubular object is initially provided on the basis of image data and local dimension information is provided for points of the representation. A subdivided division structure presentation of the tubular object with division cells is then created, which based on the local dimension information, including a different spatial extent. Finally a boundary surface network is derived on the basis of the division structure presentation. Also described are a boundary surface network determination system and a division structure determination system for performing such a method.

Method and system for determining a boundary surface network

Preoperative resection planning is assisted by a computer. Rather than rely on interpolation of the user input, a graph of interconnections is used. The user inputs one or more polylines on one or more two-dimensional views. The polylines are used to assign resection and remnant seeds with a band of unassigned locations. The 2D seeds are used with the graph of interconnections to assign different voxels in the volume, including the unassigned locations, as being part of the resection volume or part of the remnant volume.

Semi-automated preoperative resection planning

A method and system for automatically detecting liver lesions in medical image data, such as 3D CT images, is disclosed. A liver region is segmented in a 3D image. Liver lesion center candidates are detected in the segmented liver region. Lesion candidates are segmented corresponding to the liver lesion center candidates, and lesions are detected from the segmented lesion candidates using learning based verification.

Method and system for liver lesion detection

In a computerized method and system to generate visualization command data for two-dimensional visualization of a vascular tree of a vascular system from tomography data acquired via an imaging system, tomography data of the vascular tree are registered in the form of prepared tomography data that include acquired tomography data of the vascular tree in which said vascular tree is segmented at least in a region of surrounding structures, and in which are present a number of curve lines of vessels of the vascular tree that branch with one another. Inlets are shaped between curve lines that are adjacent and/or connected with one another via node points. Representation data are generated in which the inlets are arranged flat, adjoining one another in a two-dimensional presentation plane. The visualization command data are derived from the representation data and presented as an output.

Generation of visual command data

A method and system for up-vector detection for ribs in a 3D medical image volume, such as a computed tomography (CT) volume is disclosed. A rib centerline of at least one rib is extracted in a 3D medical image volume. An up-vector is automatically detected at each of a plurality of centerline points of the rib centerline of the at least one rib. The up-vector at each centerline point can be detected using a trained regression function. Alternatively, the up-vector at each centerline point can be detected by detecting an ellipse shape in a cross-sectional rib image generated at each centerline point.

Method and system for up-vector detection for ribs in computed tomography volumes

A method and system for extracting rib centerlines in a 3D volume, such as a 3D computed tomography (CT) volume, is disclosed. Rib centerline voxels are detected in the 3D volume using a learning based detector. Rib centerlines or the whole rib cage are then extracted by matching a template of rib centerlines for the whole rib cage to the 3D volume based on the detected rib centerline voxels. Each of the extracted rib centerlines are then individually refined using an active contour model.

Method and system for automatic rib centerline extraction using learning based deformable template matching

Growing community of inventors

Fuerth, Germany

Christian Tietjen