Siemens Medical Solutions USA, Inc.

Siemens Healthcare Gmbh

Siemens Healthineers Ag

Siemens Aktiengesellschaft

Gerardo Hermosillo Valadez

Yoshihisa Shinagawa

Xiang Sean Zhou

Yiqiang Zhan

Bing Jian

Luca Bogoni

Matthias Wolf

Parmeet Singh Bhatia

Zhigang Peng

Simon Allen-Raffl

Marcos Salganicoff

Christian Tietjen

Sven Kohle

Halid Yerebakan

Yu Zhao

Mahesh Ranganath

Senthil Periaswamy

Maneesh Dewan

Shu Liao

Anna Jerebko

Felix Ritter

Vandana Mohan

Ke Zeng

Yiyuan Zhao

Rainer Graumann

Berthold Kiefer

Matthias Fenchel

Arun Krishnan

Gerhard Kleinszig

Vikas Raykar

Siqi Liu

Sarang Lakare

Jan Kretschmer

Venkat Raghavan Ramamurthy

Fitsum Aklilu Reda

Michael Rusitska

Zhao Yi

Marcus Thaele

Steffen Weichert

Johannes Baeck

Felix Nensa

Saulius Archipovas

Yuping Lin

Shishir Dube

Xuguang Jiang

Chen Li

Katharina Schmidler Burk

Yimo Guo

Marcus Thäle

A computer implemented method and apparatus for determining a location at which a given feature is represented in medical imaging data is disclosed. A first descriptor for a first location in first medical imaging data is obtained. The first location is the location within the first medical imaging data at which the given feature is represented. A second descriptor for each of a plurality of candidate second locations in second medical imaging data is obtained. A similarity metric indicating a degree of similarity with the first descriptor is calculated for each of the plurality of candidate second locations. A candidate second location is selected from among the plurality of candidate second locations based on the calculated similarity metrics. The location at which the given feature is represented in the second medical imaging data is determined based on the selected candidate second location.

Determining a location at which a given feature is represented in medical imaging data

There is disclosed a method and apparatus for annotating a first portion of medical imaging data with one or more words corresponding to a respective one or more features represented in the first portion of medical imaging data. A similarity metric indicating a degree of similarity between the first portion and each of a plurality of second portions of reference medical imaging data is determined, at least one of the plurality of second portions being annotated with one or more first words corresponding to a respective one or more features represented in the second portion. A second portion is selected based on the similarity metrics, and the first portion is annotated with the one or more first words with which the second portion, selected for the first portion, is annotated.

Method and apparatus for annotating a portion of medical imaging data with one or more words

A framework for processing medical images. The framework may include receiving a target medical image, a reference medical image and at least one marker associated with a location in the reference medical image. A corresponding location of the at least one marker is determined in the target medical image. The target medical image is overlaid with the at least one marker at the determined corresponding location to provide an overlaid image. Display data is generated to cause a display device to display the overlaid image.

Method for processing medical images

A computer-implemented method is for classifying a region of interest in a medical image data set depicting a body part of a patient. The region of interest contains an object of potential pathological relevance. In an embodiment, the method includes a plurality of steps. One step is directed to generate a plurality of different representations of the region of interest. Another step is directed to determine, for each of the representations, a classification to generate a corresponding plurality of classifications. Thereby, each classification indicates a pathological relevance of the object in the respective representation. Yet, a further step is directed to calculate an ensemble classification for the region of interest based on the plurality of classifications.

Method and system for computer aided detection of abnormalities in image data

Disclosed is a computer-implemented method for detecting one or more anatomic landmarks in medical image data. In an embodiment, the method includes receiving a medical image data set depicting a body part of a patient; and determining a first set of anatomic landmarks from a first representation of the medical image data set at a first resolution by applying a first trained function to the first representation of the medical image data set. Based on that, a second set of anatomic landmarks is determined from a second representation of the medical image data set at a second resolution, the second resolution being higher than the first resolution, by applying a second trained function different than the first trained function to the second representation of the medical image data set and using the first set of landmarks by the second trained function.

Method and system for detecting landmarks in medical images

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

Disclosed is a method, a computer readable storage medium and an apparatus for processing medical image data. Input medical image data is received at a data processing system, which is an artificial intelligence-based system. An identification process is performed at the data processing system on the input medical image data to identify a volume of interest within which an instance of a predetermined anatomical structure is located. First and second determination processes are performed at the data processing system to determine, respectively, first and second anatomical directions for the instance of the anatomical structure that are defined relative to the coordinate system of the input medical image data. Output data relating to the first and second anatomical directions is output from the data processing system.

Medical image data

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

A method and for quantifying a three-dimensional medical image volume are provided. An embodiment of the method includes: providing a two-dimensional representation image based on the medical image volume; defining a region of interest in the two-dimensional representation image; generating a feature signature for the region of interest; defining a plurality of two-dimensional image patches in the medical image volume; calculating, for each of the image patches, a degree of similarity between the region of interest and the respective image patch on the basis of the feature signature; visualizing the degrees of similarities.

Spatial distribution of pathological image patterns in 3D image data

A framework for anatomy-aware adaptation of a graphical user interface. Landmarks are first detected by passing one or more current images through a trained machine learning model. A body section may then be inferred based on the detected landmarks. One or more user interface elements may be determined based on the inferred body section. A graphical user interface may then be adapted with the determined one or more user interface elements.

Anatomy-aware adaptation of graphical user interface

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 framework diagnostic test planning is described herein. In accordance with one aspect, the framework receives data representing one or more sample patients, diagnostic tests administered to the one or more sample patients, diagnostic test results and confirmed medical conditions associated with the administered diagnostic tests. The framework trains one or more classifiers based on the data to identify diagnostic test plans from the diagnostic tests. The one or more classifiers may then be applied to current patient data to generate a diagnostic test plan for a given patient.

Diagnostic test planning using machine learning techniques

Disclosed herein is a framework for facilitating image processing. In accordance with one aspect, the framework receives first image data acquired by a first modality and one or more articulated models. The one or more articulated models may include at least one section image acquired by the first modality and aligned with a local image acquired by a second modality. The framework may align an anatomical region of the first image data with the section image and non-rigidly register a first region of interest extracted from the section image with a second region of interest extracted from the aligned anatomical region. To generate a segmentation mask of the anatomical region, the registered first region of interest may be inversely mapped to a subject space of the first image data.

Anatomy aware articulated registration for image segmentation

A framework for visualization is described herein. In accordance with one implementation, one or more structures of interest are localized in a three-dimensional image. A position of an anatomical label may be determined using a positioning technique that is selected according to a view type of a visualization plane through the image, wherein the position of the anatomical label is outside the one or more structures of interest. The anatomical label may then be displayed at the determined position in the visualization plane.

Visualization of anatomical labels

Disclosed herein is a framework for facilitating synchronized image navigation. In accordance with one aspect, at least first and second medical images are received. A non-linear mapping between the first and second medical images is generated. A selection of a given location in the first medical image is received in response to a user's navigational operation. Without deforming the second medical image, a target location in the second medical image is determined by using the non-linear mapping. The target location corresponds to the given location in the first medical image. An optimized deformation-free view of the second medical image is generated based at least in part on the target location. While the user performs navigational operations on the first medical image, the framework repeatedly receives the selection of the given location, determines the target location using the non-linear mapping, and generates the optimized deformation-free view of the second medical image based at least in part on the target location.

Synchronized navigation of medical images

Growing community of inventors

West Chester, PA, United States of America

Gerardo Hermosillo Valadez