Abstract: A method for locating or identifying components or elements based on which a medical findings report can be produced for a patient to be assessed includes: providing a plurality of comparison datasets, each comparison dataset having at least one reference medical findings report; providing an analysis function, which is configured to ascertain, for a medical dataset of the patient being assessed, at least one reference dataset from the plurality of comparison datasets; ascertaining at least one reference dataset from the plurality of comparison datasets by applying the analysis function to the medical dataset and the comparison datasets; identifying at least one document model structure for the patient to be assessed based on the at least one reference medical findings report associated with the at least one reference dataset; and providing the at least one document model structure for further processing.

Abstract: Systems and computer-implemented methods are provided for identifying a template data structure for a medical report based on a medical image data set. The computer-implemented method includes: receiving a medical image data set of a patient; generating a representation of the medical image data set for display via a user interface; providing the representation for displaying to the user via the user interface; receiving, via the user interface, input directed to a medical finding visible in the representation; predicting a finding type of the medical finding based on the input; performing a lookup operation in a database to identify a template data structure for a medical report corresponding to the predicted finding type; and providing the template data structure.

Abstract: A computer-implemented method comprises: reading in sensor data relating to an image file; assigning the sensor data to a first position of a patient's anatomical structure; extracting at least one second position of the patient's anatomical structure from a medical diagnostic report; comparing the first position and the at least one second position; and outputting a warning signal if the comparison does not yield a match.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.