Abstract: A computer-implemented method and a system are for performing or supporting a medical task. An embodiment of the method includes obtaining a medical task and obtaining values for data fields of a number of available data fields. The method further includes determining whether an insufficient data field is present; and, if such a field is present, determining a relevance metric for the medical task, for the insufficient data field and/or the value thereof. Further, the method includes providing, via an estimator function, at least two different values for the insufficient data field; calculating at least two results for the medical task, which are based on the at least two different values provided; determining whether the relevance metric determined reaches or exceeds a relevance threshold value and, if this is the case, outputting an output signal based on the at least two results calculated.

Abstract: A method and system for estimating physiological heart measurements from medical images and clinical data disclosed. A patient-specific anatomical model of the heart is generated from medical image data of the patient. A patient-specific multi-physics computational heart model is generated based on the patient-specific anatomical model by personalizing parameters of a cardiac electrophysiology model, a cardiac biomechanics model, and a cardiac hemodynamics model based on medical image data and clinical measurements of the patient. Cardiac function of the patient is simulated using the patient-specific multi-physics computational heart model. The parameters can be personalized by inverse problem algorithms based on forward model simulations or the parameters can be personalized using a machine-learning based statistical model.

Abstract: Intelligent multi-scale image parsing determines the optimal size of each observation by an artificial agent at a given point in time while searching for the anatomical landmark. The artificial agent begins searching image data with a coarse field-of-view and iteratively decreases the field-of-view to locate the anatomical landmark. After searching at a coarse field-of view, the artificial agent increases resolution to a finer field-of-view to analyze context and appearance factors to converge on the anatomical landmark. The artificial agent determines applicable context and appearance factors at each effective scale.

Abstract: 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.

Abstract: Intelligent multi-scale image parsing determines the optimal size of each observation by an artificial agent at a given point in time while searching for the anatomical landmark. The artificial agent begins searching image data with a coarse field-of-view and iteratively decreases the field-of-view to locate the anatomical landmark. After searching at a coarse field-of view, the artificial agent increases resolution to a finer field-of-view to analyze context and appearance factors to converge on the anatomical landmark. The artificial agent determines applicable context and appearance factors at each effective scale.

Abstract: An embodiment of a method includes providing a first result list indicating a plurality of first anatomic structures and indicating, for each respective first anatomic structure of the plurality of first anatomic structures, a corresponding first severity indicator; providing a second result list indicating, for each respective second anatomic structure of the plurality of the second anatomic structures, a corresponding second severity indicator; providing a relationship matrix indicating a level of interrelatedness between the first anatomic structures and the second anatomic structures; and generating, based on the first result list provided, on the second result list and on the relationship matrix provided, a concordance visualization indicating a respective level of concordance between at least one of the first anatomic structures and the corresponding first severity indicator, and indicating a respective level of concordance between at least one of the second anatomic structures and the corresponding sec

Abstract: A method and system for estimating tissue parameters of a computational model of organ function and their uncertainty due to model assumptions, data noise and optimization limitations is disclosed. As applied to a cardiac use-case, a patient-specific anatomical heart model is generated from medical image data of a patient. A patient-specific computational heart model is generated based on the patient-specific anatomical heart model. Patient-specific parameters and corresponding uncertainty values are estimated for at least a subset of parameters of the patient-specific computational heart model. A surrogate model is estimated for a forward model of cardiac function, and the surrogate model is applied within Bayesian inference to estimate the posterior probability density function of the parameter space of the forward model. Cardiac function for the patient is simulated using the patient-specific computational heart model.

Abstract: A method of generating a first image from a medical text report comprises acquiring a medical text report comprising one or more natural language statements; analysing the medical text report, using a computer-implemented analysis process, to determine for each natural language statement whether the statement satisfies a predetermined criterion with respect to a first medical finding; and responsive to a determination that a said statement satisfies the predetermined criterion, adding an image representing the first medical finding to an image template, to generate the first image. Also disclosed is an apparatus and computer program.

Abstract: A method for providing a secondary parameter, a decision support system, a computer-readable medium and a computer program product are disclosed. In an embodiment, the method is for providing a secondary parameter in a decision support system providing a primary parameter, in particular in a clinical decision support system. The method includes: providing an input data set; approximating a secondary parameter based on the input data set by using a sub-system being trained by a machine learning mechanism, in particular by a deep learning mechanism; and providing the approximated secondary parameter. The input data set is a shape data set.

Abstract: Intelligent image parsing for anatomical landmarks and/or organs detection and/or segmentation is provided. A state space of an artificial agent is specified for discrete portions of a test image. A set of actions is determined, each specifying a possible change in a parametric space with respect to the test image. A reward system is established based on applying each action of the set of actions and based on at least one target state. The artificial agent learns an optimal action-value function approximator specifying the behavior of the artificial agent to maximize a cumulative future reward value of the reward system. The behavior of the artificial agent is a sequence of actions moving the agent towards at least one target state. The learned artificial agent is applied on a test image to automatically parse image content.

Abstract: A method and system for transcatheter aortic valve implantation (TAVI) planning is disclosed. An anatomical surface model of the aortic valve is estimated from medical image data of a patient. Calcified lesions within the aortic valve are segmented in the medical image data. A combined volumetric model of the aortic valve and calcified lesions is generated. A 3D printed model of the heart valve and calcified lesions is created using a 3D printer. Different implant device types and sizes can be placed into the 3D printed model of the aortic valve and calcified lesions to select an implant device type and size for the patient for a TAVI procedure. The method can be similarly applied to other heart valves for any type of heart valve intervention planning.

Abstract: Intelligent multi-scale image parsing determines the optimal size of each observation by an artificial agent at a given point in time while searching for the anatomical landmark. The artificial agent begins searching image data with a coarse field-of-view and iteratively decreases the field-of-view to locate the anatomical landmark. After searching at a coarse field-of view, the artificial agent increases resolution to a finer field-of-view to analyze context and appearance factors to converge on the anatomical landmark. The artificial agent determines applicable context and appearance factors at each effective scale.

Abstract: A method is for segmenting an object in a medical image with a plurality of iteration steps. In an embodiment of the method, each iteration step includes generating a plurality of patches, a portion of the input image and a patch location being assigned to each patch, the patch location being indicative of the location of the portion of the input image relative to the input image. For each patch of the plurality of patches, the method includes determining a vote location based on the portion of the input image assigned to that patch and determining a target location based on the vote location and the patch location assigned to that patch. Finally, in an embodiment the method includes generating a vote map, each patch of the plurality of patches contributing to a pixel value at the target location of the patch in the vote map.

Abstract: Intelligent multi-scale image parsing determines the optimal size of each observation by an artificial agent at a given point in time while searching for the anatomical landmark. The artificial agent begins searching image data with a coarse field-of-view and iteratively decreases the field-of-view to locate the anatomical landmark. After searching at a coarse field-of view, the artificial agent increases resolution to a finer field-of-view to analyze context and appearance factors to converge on the anatomical landmark. The artificial agent determines applicable context and appearance factors at each effective scale.

Abstract: Intelligent multi-scale image parsing determines the optimal size of each observation by an artificial agent at a given point in time while searching for the anatomical landmark. The artificial agent begins searching image data with a coarse field-of-view and iteratively decreases the field-of-view to locate the anatomical landmark. After searching at a coarse field-of view, the artificial agent increases resolution to a finer field-of-view to analyze context and appearance factors to converge on the anatomical landmark. The artificial agent determines applicable context and appearance factors at each effective scale.

Abstract: Intelligent image parsing for anatomical landmarks and/or organs detection and/or segmentation is provided. A state space of an artificial agent is specified for discrete portions of a test image. A set of actions is determined, each specifying a possible change in a parametric space with respect to the test image. A reward system is established based on applying each action of the set of actions and based on at least one target state. The artificial agent learns an optimal action-value function approximator specifying the behavior of the artificial agent to maximize a cumulative future reward value of the reward system. The behavior of the artificial agent is a sequence of actions moving the agent towards at least one target state. The learned artificial agent is applied on a test image to automatically parse image content.

Abstract: Using computational models for the patient physiology and the various therapy options, a decision support system presents a range of predicted outcomes to assist in planning the therapy. The models are used in various experiments for the many therapy options to determine an optimal approach.

Abstract: Intelligent image parsing for anatomical landmarks and/or organs detection and/or segmentation is provided. A state space of an artificial agent is specified for discrete portions of a test image. A set of actions is determined, each specifying a possible change in a parametric space with respect to the test image. A reward system is established based on applying each action of the set of actions and based on at least one target state. The artificial agent learns an optimal action-value function approximator specifying the behavior of the artificial agent to maximize a cumulative future reward value of the reward system. The behavior of the artificial agent is a sequence of actions moving the agent towards at least one target state. The learned artificial agent is applied on a test image to automatically parse image content.

Abstract: A method and system for transcatheter aortic valve implantation (TAVI) planning is disclosed. An anatomical surface model of the aortic valve is estimated from medical image data of a patient. Calcified lesions within the aortic valve are segmented in the medical image data. A combined volumetric model of the aortic valve and calcified lesions is generated. A 3D printed model of the heart valve and calcified lesions is created using a 3D printer. Different implant device types and sizes can be placed into the 3D printed model of the aortic valve and calcified lesions to select an implant device type and size for the patient for a TAVI procedure. The method can be similarly applied to other heart valves for any type of heart valve intervention planning.

Abstract: A method and system for estimating physiological heart measurements from medical images and clinical data disclosed. A patient-specific anatomical model of the heart is generated from medical image data of the patient. A patient-specific multi-physics computational heart model is generated based on the patient-specific anatomical model by personalizing parameters of a cardiac electrophysiology model, a cardiac biomechanics model, and a cardiac hemodynamics model based on medical image data and clinical measurements of the patient. Cardiac function of the patient is simulated using the patient-specific multi-physics computational heart model. The parameters can be personalized by inverse problem algorithms based on forward model simulations or the parameters can be personalized using a machine-learning based statistical model.