Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Siemens Medical Solutions Usa, Inc.

Siemens Corporation

Siemens Healthineers Ag

Martin Huber

Dorin Comaniciu

Michael Wels

Michael Rusitska

Michael Suehling

Michael Kelm

Andrew John Hewett

Sascha Seifert

Gerhard Kohl

Alexander Cavallaro

Bogdan Georgescu

Shaohua Kevin Zhou

Razvan Ioan Ionasec

Dime Vitanovski

Maria Jimena Costa

Gustavo Henrique Monteiro De Barros Carneiro

Markus Metz

Yefeng Zheng

Xiang Sean Zhou

David D Liu

Arun Krishnan

Grzegorz Soza

Adrian Barbu

Ingmar Voigt

Karlheinz Glaser-Seidnitzer

Sven Kohle

Helmut König

Alexey Tsymbal

Thomas Gossler

Rüdiger Bertsch

Roland Brill

Johannes Feulner

James Pressler

Joerg Freund

Christoph Speier

Nadine Liebs

Marcus Thäle

A user interacts with a medical imaging system and a medical reporting system to prepare a medical report. The medical reporting system includes report templates for medical reports. The report templates include placeholder fields with unique placeholder designations and metadata. After a user entry in the medical imaging system to select an image dataset, the image dataset and analysis tools are made available to the user. After a user entry in the medical reporting system to select a report template, a report template is made available. Placeholders contained in the selected report template are displayed with corresponding placeholder designations and metadata, automatically sent to the imaging system, and displayed as data requests. According to the data requests displayed, the data corresponding to the data requests is ascertained or determined by the user and assigned to the placeholder designations. A report editor automatically saves the data in the placeholder fields.

Method, apparatus, and computer program product for preparing a medical report

Computer-assisted structuring of medical examination data and/or one or more examination data records is disclosed. A method of at least one embodiment includes providing at least one medical examination data record, which includes patient-specific data described textually and/or symbolically and/or at least one image data record created with the aid of a radiological examination device; providing at least one body model image, which represents a body model matching the examination data; and registering the at least one examination data record with the body model, wherein at least one position in the body model is assigned to the examination data record; the position being made known for interaction by way of a user interface.

Method and arrangement for computer-assisted representation and/or evaluation of medical examination data

A method and system for segmenting multiple brain structures in 3D magnetic resonance (MR) images is disclosed. After intensity standardization of a 3D MR image, a meta-structure including center positions of multiple brain structures is detected in the 3D MR image. The brain structures are then individually segmented using marginal space learning (MSL) constrained by the detected meta-structure.

Method and system for segmentation of brain structures in 3D magnetic resonance images

A method and apparatus for hierarchical parsing and semantic navigation of a full or partial body computed tomography CT scan is disclosed. In particular, organs are segmented and anatomic landmarks are detected in a full or partial body CT volume. One or more predetermined slices of the CT volume are detected. A plurality of anatomic landmarks and organ centers are then detected in the CT volume using a discriminative anatomical network, each detected in a portion of the CT volume constrained by at least one of the detected slices. A plurality of organs, such as heart, liver, kidneys, spleen, bladder, and prostate, are detected in a sense of a bounding box and segmented in the CT volume, detection of each organ bounding box constrained by the detected organ centers and anatomic landmarks. Organ segmentation is via a database-guided segmentation method.

Method and system for hierarchical parsing and semantic navigation of full body computed tomography data

A method and system for the diagnosis of 3D images are disclosed, which significantly cuts the time required for the diagnosis. The 3D images are for example an image volume dataset of a magnetic resonance tomography system which is saved in an RIS or PACS system. In at least one embodiment, the diagnostic finding are partially automatically generated, and details of the position, size and change in pathological structures are compared to previous diagnostic findings are generated automatically. As a result of this automation the diagnostic work of radiologists is significantly reduced.

Computer-aided evaluation of an image dataset

A document and template creation system includes a document and template creation device. The document and template creation device is configured to identify at least one domain ontology concept based on at least a portion of a text-string input into a document, propose the at least one domain ontology concept for selection by the user, and insert at least one of the at least one domain ontology concept into the document in response to selection by the user.

Methods, apparatuses, systems and computer readable mediums to create documents and templates using domain ontology concepts

One embodiment of the invention is disclosed, related to a method for associating at least two different medical findings with each other, each of the findings being based on a characteristic area in a different set of images. The location of at least one anatomical landmark in relation to the respective characteristic area is determined in each in each of the different sets of images. Then a reference to the least one anatomical landmark is stored together with the respective finding for each of the at least two findings. Finally, pairs of associated findings based on a comparison of the respective references to the at least one anatomical landmark are identified among the at least two findings.

Method and system for associating at least two different medical findings with each other

A method and system for fully automatic segmentation the prostate in multi-spectral 3D magnetic resonance (MR) image data having one or more scalar intensity values per voxel is disclosed. After intensity standardization of multi-spectral 3D MR image data, a prostate boundary is detected in the multi-spectral 3D MR image data using marginal space learning (MSL). The detected prostate boundary is refined using one or more trained boundary detectors. The detected prostate boundary can be split into patches corresponding to anatomical regions of the prostate and the detected prostate boundary can be refined using trained boundary detectors corresponding to the patches.

Method and system for segmentation of the prostate in 3D magnetic resonance images

A method and system for providing medical decision support based on virtual organ models and learning based discriminative distance functions is disclosed. A patient-specific virtual organ model is generated from medical image data of a patient. One or more similar organ models to the patient-specific organ model are retrieved from a plurality of previously stored virtual organ models using a learned discriminative distance function. The patient-specific valve model can be classified into a first class or a second class based on the previously stored organ models determined to be similar to the patient-specific organ model.

Method and system for medical decision support using organ models and learning based discriminative distance functions

A method and system for modeling the pulmonary trunk in 4D image data, such as 4D CT data, and model-based percutaneous pulmonary valve implantation (PPVI) intervention is disclosed. A patient-specific dynamic pulmonary trunk data is generated from 4D image data of a patient. The patient is automatically classified as suitable for PPVI intervention or not suitable for PPVI intervention based on the generated patient-specific dynamic pulmonary trunk model.

Method and system for dynamic pulmonary trunk modeling and intervention planning

A method and system for automatic semantics driven registration of medical images is disclosed. Anatomic landmarks and organs are detected in a first image and a second image. Pathologies are also detected in the first image and the second image. Semantic information is automatically extracted from text-based documents associated with the first and second images, and the second image is registered to the first image based the detected anatomic landmarks, organs, and pathologies, and the extracted semantic information.

Method and system for semantics driven image registration

A method and system for brain tumor segmentation in multi-spectral 3D MRI images is disclosed. A trained probabilistic boosting tree (PBT) classifier is used to determine, for each voxel in a multi-spectral 3D MR image sequence, a probability that the voxel is part of a brain tumor. The brain tumor is then segmented in the multi-spectral 3D MRI image sequence using graph cuts segmentation based on the probabilities determined using the trained PBT classifier and intensities of the voxels in the multi-spectral 3D MR image sequence.

Method and system for brain tumor segmentation in 3D magnetic resonance images

A method for training a computer system for automatic detection of regions of interest includes receiving patient records. For each of the received patient records a text field and a medical image are identified from within the patient record and the medical image is automatically segmented to identify a structure of interest. The text field is searched for one or more keywords indicative of a particular abnormality associated with the structure of interest. The medical image is added to a grouping representing the particular abnormality when the text field indicates that the patient has the particular abnormality and the medical image is added to a grouping representing the absence of the particular abnormality when the text field does not indicate that the patient has the particular abnormality. The groupings of medical images are used to automatically train a computer system for the subsequent detection of the particular abnormality.

Automatic learning of image features to predict disease

Growing community of inventors

Uttenreuth, Germany

Martin Huber