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.

Abstract: A system (100) and computer-implemented method are provided for data collection for distributed machine learning of a machine learnable model. A privacy policy data (050) is provided defining computer-readable criteria for limiting a selection of medical image data (030) to a subset of the medical image data to obfuscate an identity of the at least one patient. The medical image data is selected based on the computer-readable criteria to obtain privacy policy-compliant training data (060) for transmission to another entity. The system and method enable medical data collection at clinical sites without requiring manual oversight, and enables such selections to be made automatically, e.g., based on a request for medical image data which may be received from outside of the clinical site.

Abstract: Disclosed herein is a medical system (100, 300) comprising a display (112) and a user interface (114). The execution of machine executable instructions (120) causes a processor (104) to: receive (200) three-dimensional medical image data (122) of an anatomical structure (128, 322); receive (202) a three-dimensional segmentation (124) with one or more reference locations (800); display (204) at least one two-dimensional slice (126) of the three-dimensional medical image data; render (206) a cross section (134) of the three-dimensional segmentation, provide (208) a control element (130) of the user interface that is configured for receiving a one-dimensional position of the at least one reference location along a predetermined one-dimensional path (806); receive (210) the one-dimensional position (137) from the control element; adjust (212) the three-dimensional segmentation (124) using the one-dimensional position; and update (214) the rendering of the cross section of the three-dimensional segmentation.

Abstract: An information retrieval system (IPS). The system comprises an input interface (IN) for receiving a query related to an object of interest. A concept mapper (CM) is configured to map the query to one or more associated concept entries of a hierarchic graph data structure (ONTO). The entries in said structure encode linguistic descriptors of components of a model (GM) for said object (OB). A metric-mapper (MM) is configured to map the query to one or more metric relationship descriptors. A geo-mapper (GEO) is configured to map said concept entries against the geometric model linked to the hierarchic graph data structure to obtain spatio-numerical data associated with said linguistic descriptors. A metric component (MTC) is configured to compute one or more metric or spatial relationships between said object components based on the spatio-numerical data and the one or more metric relationship descriptors.

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: 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: A system and method are provided for display of medical image data, with the display of the medical image data being determined on the basis of schematic image data of a schematic representation of an anatomical structure. The schematic representation may provide a particular view of the anatomical structure. The type of anatomical structure and the view of the anatomical structure provided by the schematic representation may be determined based on one or more image features in the schematic image data. The view may be characterized as a geometrically-defined perspective at which the anatomical structure is shown in the schematic representation. An output image may be generated showing the anatomical structure in the medical image data in accordance with said determined geometrically-defined perspective. A user may thus be provided with a display of medical image data which is easier to interpret having considered said schematic representation.

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 (SYS) for supporting a medical procedure, comprising an interface (IN) for receiving at least one medical input signal that describes a state of a target anatomy. A signal analyzer (SA) is configured to analyze the medical input signal to determine a time window for deployment of a cardio-vascular device (CL) to be deployed by a deployment.

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: The invention relates to a system for assisting in evaluating a contour of an anatomic structure (22) with respect to a dose distribution corresponding to a treatment plan for a radiation therapy treatment of a patient. The system comprises an evaluation unit particularly configured to evaluate the dose distribution in varying distances from the contour of the anatomic structure (22) to determine at least one point where the evaluated dose distribution fulfills a predetermined condition, and to determine the distance between the at least one point and the contour and/or to visualize the at least one point to a user of the system.

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: Slice alignment approaches are described for short axis cardiac magnetic resonance cine slice stacks, which do not require additional scans, such as long axis scans or full 3D scans, and which are able to deal with cardiac structures having complex shapes. Both approaches do not need contours to follow a quadratic curvature function, and are well suitable for the purpose of obtaining a segmentation of a cardiac structure using a deformable surface model. Namely, such a deformable surface model is unable, but also not desired, to fully adapt to the ‘zig-zag’-shaped pattern in the boundary of the cardiac structure due to the slice misalignment. Having removed or reduced the misalignment between image slices, such a deformable surface model may better adapt to the cardiac structure in the image data and 10 thereby provide a better segmentation of the cardiac structure.

Abstract: There is provided a method and apparatus for refining a model of an anatomical structure in an image. A model for the anatomical structure in the image is acquired. The model comprises a plurality of control points, each control point corresponding to a feature in the anatomical structure. The model is placed in the image with respect to the anatomical structure. Based on a user input received to adjust the model in the image, a position of at least one of the plurality of control points is adjusted to alter a shape of the model to the anatomical structure in the image, wherein adjustment of the position of one or more of the at least one control points is restricted based on information relating to the at least one control point.

Abstract: A model may be trained on a training dataset, e.g., for medical image processing or medical signal processing tasks. Systems and computer-implemented methods are provided for associating a population descriptor with the trained model and using the population descriptor to determine whether records to which the model is to be applied, conform to the population descriptor. The population descriptor characterizes a distribution of the one or more characteristic features over the training dataset, with the characteristic features characterizing the training record and/or a model output provided when the trained model is applied to the training record. For instance, the model may be applied only to records conforming to population descriptor, or model outputs of applying the model to non-conforming records may be flagged as possibly untrustworthy.

Abstract: The invention provides for a medical imaging system (100, 300, 400) comprising a memory (110) containing machine executable instructions (120) and a processor (106). Execution of the machine executable instructions cause the processor to: receive (200) a magnetic resonance image (300), receive (202) meta data descriptive of the magnetic resonance image, wherein the metadata comprises reference gray scale value data (124) for two or more tissue types; and segment (204) the magnetic resonance image using an image segmentation algorithm (126) that uses the reference gray scale value data.

Abstract: A prediction model is provided which is capable of predicting a correctness of a segmentation by a segmentation algorithm. The prediction model may be trained using a machine learning technique, and after training used to predict the correctness of a segmentation of a boundary in respective image portions of an image by the segmentation algorithm. The predicted correctness may then be visualized, for example as an overlay of the segmentation.

Abstract: There is provided a method and apparatus for refining a model of an anatomical structure in an image. A model for the anatomical structure in the image is acquired. The model comprises a plurality of control points, each control point corresponding to a feature in the anatomical structure. The model is placed in the image with respect to the anatomical structure. Based on a user input received to adjust the model in the image, a position of at least one of the plurality of control points is adjusted to alter a shape of the model to the anatomical structure in the image, wherein adjustment of the position of one or more of the at least one control points is restricted based on information relating to the at least one control point.

Abstract: There is provided a method and apparatus for segmenting two-dimensional images of an anatomical structure. A time sequence of two-dimensional images of the anatomical structure is acquired (202) and a segmentation model for the anatomical structure is acquired (204). The segmentation model comprises a plurality of segments. The acquired segmentation model is applied to the entire time sequence of two-dimensional images of the anatomical structure simultaneously in time and space to segment the time sequence of two-dimensional images by way of the plurality of segments (206).

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.