Abstract: The invention relates to a system and computer-implemented method for enabling correction of a segmentation of an anatomical structure in 3D image data. The segmentation may be provided by a mesh which is applied to the 3D image data to segment the anatomical structure. The correction may for example involve a user directly or indirectly selecting a mesh part, such as a mesh point, that needs to be corrected. The behaviour of the correction, e.g., in terms of direction, radius/neighbourhood or strength, may then be dependent on the mesh normal direction, and in some embodiments, on a difference between the mesh normal direction and the orientation of the viewing plane.

Abstract: A computing system (SYS) and relates method for supporting radiation therapy planning. An input interface (IF1; UI-I) is provided for receiving input radiation treatment plan templates, or types of such plan templates, for plural radiation treatment plans. A trained machine learning model (M) of the system (SYS) is configured to compute plural dose maps associated with the received radiation treatment plan templates or with the types of such plan templates.

Abstract: A device for optimizing a radiation therapy plan (30) for delivering therapeutic radiation to a patient using a therapeutic radiation source (16) while modulated by a multi-leaf collimator (MLC) (14) includes at least one electronic processor (25) connected to a radiation therapy device (12). A non-transitory computer readable medium (26) stores instructions readable and executable by the at least one electronic processor to perform a radiation therapy plan optimization method (102) including: optimizing MLC settings of the MLC respective to an objective function wherein the MLC settings define MLC leaf tip positions for a plurality of rows of MLC leaf pairs at a plurality of control points (CPs). The optimizing is performed in two or more iterations with a resolution of the MLC settings increasing in successive iterations.

Abstract: According to an aspect, there is provided a computer implemented method for boundary detection of an object of interest in an image (200), the method comprising: for a volume of the image corresponding to a portion of a three-dimensional. 3D, mesh, representing the object of interest, predicting, by a regression network, at least one predicted distance from the portion of the 3D mesh to a boundary of the object of interest in the image, the at least one predicted distance corresponding to a class (202); and determining a distance of the portion of the 3D mesh to the boundary based on at least one probability of the volume corresponding to a class and the at least one predicted distance (204).

Abstract: The invention relates to a system for determining a radiation therapy plan for a radiation therapy system (100), comprising a multi-leaf collimator. The radiation therapy plan determination system (110) comprises a therapy system characteristics providing unit (111), wherein the characteristics comprise possible leaf positions and possible radiation fluence values, a planning objectives providing unit (112), wherein the planning objectives are indicative of a desired therapeutic radiation dose distribution, an optimization function providing unit (113), wherein the optimization function is indicative of a deviation of a radiation dose distribution from the planning objectives and of an uncertainty of the radiation dose distribution at edges of the possible apertures, and a therapy plan optimization unit (114) adapted to determine a sequence of possible apertures and possible radiation fluence values for which the optimization function is optimized.

Abstract: The present invention relates to segmentation. In order to provide a facilitated way of providing enhanced segmentation, a device (10) for enhanced segmentation via an interface is provided. The device comprises a data processor (12), and a data exchange interface (14). The data processor is configured to provide a base segmentation of a region of interest of a subject; wherein the base segmentation comprises a base plurality of information; to generate a reduced segmentation from the base segmentation; wherein the reduced segmentation comprises a reduced plurality of information; and wherein the reduced plurality of information comprises less information than the base plurality of information. The data exchange interface is configured to provide the reduced segmentation to a secondary data processing (18) for further processing to generate secondary data.

Abstract: A device for optimizing a radiation therapy plan (30) for delivering therapeutic radiation to a patient using a therapeutic radiation source (16) while modulated by a multi-leaf collimator (MLC) (14) includes at least one electronic processor (25) connected to a radiation therapy device (12). A non-transitory computer readable medium (26) stores instructions readable and executable by the at least one electronic processor to perform a radiation therapy plan optimization method (102) including: optimizing MLC settings of the MLC respective to an objective function wherein the MLC settings define MLC leaf tip positions for a plurality of rows of MLC leaf pairs at a plurality of control points (CPs). The optimizing is performed in two or more iterations with a resolution of the MLC settings increasing in successive iterations.

Abstract: A mechanism for generating information usable for identifying a risk of arrhythmia. A plurality of heart models are constructed from medical imaging data of a subject's heart. each heart model being defined by a set of different values for one or more modifiable properties. The modifiable properties are those used in the generation of the heart model or in the definition of the heart model, and influence or affect simulation results using the heart model. Each heart model undergoes a plurality of simulations, each simulation being a simulation of a response of the heart model to a (simulated) stimulation of electricity at a different pacing location of the heart. Output data is generated containing indicators of all simulation results.

Abstract: The present invention relates to providing image data of adjustable anatomical planes. In order to provide a more user friendly way of images in selected viewing planes, a device (10) for providing image data of adjustable anatomical planes is provided that comprises a data input (12), a data processor (14), a user interface (16) and an output interface (18). The data input is configured to receive 3D image data of a region of interest of a subject. The data processor is configured to establish anatomical context for identifying anatomical reference locations within the 3D image data. The data processor is also configured to generate a reference coordinate system based on the identified anatomical reference locations. The data processor is also configured to define an anchor point. The data processor is further configured to compute a viewing plane as a selected anatomical imaging plane based on the anchor point and at least one plane-related parameter.

Abstract: An ultrasound imaging system having an image processor configured to receive at volume data resulting from a three-dimensional ultrasound scan of a body and to provide corresponding display data, an anatomy detector configured to detect a position and orientation of an anatomical object of interest within the volume data, a slice generator for generating a plurality of two-dimensional slices from the volume data, wherein the slice generator is configured to define respective slice locations based on the results of the anatomy detector for the anatomical object of interest so as to obtain a set of two-dimensional standard views of the anatomical object of interest, wherein the slice generator is further configured to define for each two-dimensional standard view which anatomical features of the anatomical object of interest are expected to be contained. An evaluation unit evaluates a quality factor for each of the two-dimensional slices.

Abstract: The invention relates a system for assisting in planning a radiation therapy treatment provided using a treatment plan comprising irradiation parameters for controlling a delivery of radiation. The system is configured to (i) receive a first dose distribution, (ii) obtain a first objective function, which depends upon at least one parameter and a dose distribution, (iii) determine a first value of the parameter such that the first objective function fulfills a predefined criterion when being evaluated for the first value of the parameter and for a second dose distribution derived from the first dose distribution, (iii) provide the first objective function in connection with the first value of the at least one parameter to a user for modifying the first objective function to generate a second objective function, and (v) determine the treatment plan using the second objective function. Further, the invention relates to a corresponding method and computer program.

Abstract: The invention relates to a planning apparatus for planning a radiation therapy. A medical image, in which a target to be irradiated is indicated, is reformatted based on ray geometries to be used during the radiation therapy to be planned, resulting in several reformatted medical images. Radiation therapy parameters being indicative of intensities of rays 5 to be used for irradiating a target 4 are determined based on the reformatted medical images by using a neural network unit. This allows to determine high quality radiation therapy parameters and hence allows for an improved planning of a radiation therapy. In particular, radiation and absorptions physics can be captured better, which can lead to the improved quality.

Abstract: The present invention relates to multispectral imaging. In order to improve an identification of relevant multispectral material transitions (in particular caused by injected contrast agent), an apparatus is proposed to use the local maxima of the variances and/or covariances of the intensities of the multi-channel images to locate material transitions. In comparison to gradient vectors, the local variance is not directed and not prone to noise. An alternative apparatus is proposed to use the local covariance deficits of the intensities of the multi-channel images to locate material transitions. The proposed alternative approach is independent of spatial drifts across the image volume.

Abstract: A system for establishing a contour of a structure is disclosed. An initialization subsystem (1) is used for initializing an adaptive mesh representing an approximate contour of the structure, the structure being represented at least partly by a first image, and the structure being represented at least partly by a second image. A deforming subsystem (2) is used for deforming the adaptive mesh, based on feature information of the first image and feature information of the second image. The deforming subsystem comprises a force-establishing subsystem (3) for establishing a force acting on at least part of the adaptive mesh, in dependence on the feature information of the first image and the feature information of the second image. A transform-establishing subsystem (4) is used for establishing a coordinate transform reflecting a registration mismatch between the first image, the second image, and the adaptive mesh.

Abstract: A system and computer-implemented method are provided for annotation of image data. A user is enabled to iteratively annotate the image data. An iteration of said iterative annotation comprises generating labels for a current image data part based on user-verified labels of a previous image data part, and enabling the user to verify and correct said generated labels to obtain user-verified labels for the current image data part. The labels for the current image data part are generated by combining respective outputs of a label propagation algorithm and a machine-learned classifier trained on user-verified labels and image data and applied to image data of the current image data part. The machine-learned classifier is retrained using the user-verified labels and the image data of the current image data part to obtain a retrained machine-learned classifier.

Abstract: A system and method for training a deep learning network with previously read image studies to provide a prioritized worklist of unread image studies. The method includes collecting training data including a plurality of previously read image studies, each of the previously read image studies including a classification of findings and radiologist-specific data. The method includes training the deep learning neural network with the training data to predict an urgency score for reading of an unread image study.

Abstract: A system and computer-implemented method are provided for preprocessing medical image data for machine learning. Image data is accessed which comprises an anatomical structure. The anatomical structure in the image data is segmented to obtain a segmentation of the anatomical structure as a delineated part of the image data. A grid is assigned to the delineated part of the image data, the grid representing a partitioning of an exterior and interior of the type of anatomical structure using grid lines, wherein said assigning comprises adapting the grid to fit the segmentation of the anatomical structure in the image data. A machine learning algorithm is then provided with an addressing to the image data in the delineated part on the basis of coordinates in the assigned grid. In some embodiments, the image data of the anatomical structure may be resampled using the assigned grid.

Abstract: Systems and methods are provided for generating and using statistical data which is indicative of a difference in shape of a type of anatomical structure between images acquired by a first imaging modality and images acquired by a second imaging modality. This statistical data may then be used to modify a first segmentation of the anatomical structure which is obtained from an image acquired by the first imaging modality so as to predict the shape of the anatomical structure in the second imaging modality, or in general, to generate a second segmentation of the anatomical structure as it may appear in the second imaging modality based on the statistical data and the first segmentation.

Abstract: A system and method are provided for use in evaluating a clinical guideline which is represented in a machine readable version by a decision tree comprising at least one node and a decision rule associated with the node. The decision rule comprises at least one variable representing a biomedical quantity. The biomedical quantity is extracted from the patient data using an ontology which defines concepts and their relationships in a medical domain of the clinical guideline and which thereby relates the variable of the decision rule to the patient data. If said extraction is not possible, a view of the patient data is presented to the user to enable the user to determine the biomedical quantity from the view. Advantageously, the user is assisted in evaluating the clinical guideline even when it is not possible to automatically extract the biomedical quantity from the patient data.

Abstract: The present application describes a computing system, a computer readable medium, and/or related method for supporting decision making in adaptive therapy. An input interface of receives an input image. A machine learning module predicts, based at least in part on the input image, a dose distribution associated with a first planning technique or a first treatment modality. A comparator compares a planned dose distribution as per a current treatment plan with the predicted dose distribution, to obtain a comparison result. The comparison result enables a user to gauge whether an actual re-planning would yield a dosimetric benefit before committing time or computational resources.