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:
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. Advantageous, a standardized addressing to the image data of the anatomical structure is provided, which may reduce the computational overhead of the machine learning, require fewer training data, etc.
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:
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:
Image processing apparatus 100 comprising an input 110 for obtaining an image 102 and segmentation data 112 configured for use in segmenting a region of interest in a predetermined type of image, the input being arranged for further obtaining a segmentation data descriptor 116 of the segmentation data, the segmentation data descriptor being indicative of the predetermined type of image, and a processor 120 for (i) obtaining, based on the image, an image descriptor indicative of an actual type of the image, (ii) comparing the image descriptor to the segmentation data descriptor, and (iii) establishing, based on said comparing, a suitability indication 122 for using the segmentation data in segmenting the region of interest in the image to avoid the use of the segmentation data when the predetermined type of image insufficiently matches the actual type of the image.
Abstract:
The invention relates to an apparatus, a method and a computer program for visualizing a conduction tract of a heart. In order to provide a visualization which is helpful in avoiding or finding the conduction tract in, for example, an invasive procedure like ablation of heart tissue, a generic heart model is adapted to geometrical data of the patient's heart, wherein model data indicating a shape and/or position of the conduction tract is modifying in accordance to the adaptation. The modification of the model data is further refined based on electrophysiological data and the refined model is used for generating a visualization.
Abstract:
According to an aspect, there is provided a computer-implemented method of operating a visual data delivery system. The method comprises: processing (901) a sequence of 3-dimensional, 3D, images of a body to generate first 2-dimensional, 2D, image data representing a first sequence of 2D images of the body, wherein the 2D images are images of the body in a 2D image plane through the 3D images, and wherein an amount of data representing the first 2D image data is less than an amount of data representing the 3D images from which the first 2D image data is generated; sending (903) the first 2D image data to a display system for display of the first sequence of 2D images of the body by the display system; receiving (905) a 2D image plane adjustment indication from the display system, wherein the 2D image plane adjustment indication indicates a required rotation and/or translation of the 2D image plane; processing (907) the sequence of 3D images and/or a further sequence of 3D images to generate second 2D image data representing a second sequence of 2D images of the body, wherein the 2D images in the second sequence of 2D images are images of the body in the rotated and/or translated 2D image plane; and sending (909) the second 2D image data to the display system for display of the second sequence of 2D images of the body by the display system.
Abstract:
A system and method for achieving more accurate results when applying an image processing task to a series of medical images of a patient, without significantly increasing processing resource. The proposed system and method is based on receiving a plurality of image sequences of a particular anatomical region, each capturing cyclical movement of an anatomical object. Each image sequence is supplied to a classifier module which employs use of one or more machine learning algorithms to derive at least one score for each image sequence indicative of predicted success or quality of a result of the image processing task if applied to the given image series. This permits an assessment to be made in advance of which of the plurality of image series is most likely to result in the best (e.g. highest quality, or greatest amount of information) results from the image processing task. This allows maximization of the quality of image processing results, without the need to actually process each of the image series with the image processing task, which would consume a large amount of processing resource and consume time.
Abstract:
A system (100) for detecting and labeling structures of interest includes a current patient study database (102) containing a current patient study (200) with clinical contextual information (706), a statistical model patient report database (104) containing at least one or more prior patient documents containing clinical contextual information (706), an image metadata processing engine (118) configured to extract metadata for preparing an input for an anatomical structure classifier (608), a natural language processing engine (120) configured to extract clinical context information (706) from the prior patient documents, an anatomical structure detection and labeling engine (718) or processor (112), and a display device (108) configured to display findings from the current patient study. The anatomical structure detection and labeling engine (718) or processor (112) is configured to identify and label one or more structures of interest (716) from the extracted metadata and clinical context information (706) and aggregate series level data.
Abstract:
A system (100) is provided for determining a transformation between different coordinate systems associated with different medical data. In determining the transformation, the system (100) makes use of a third set of anatomical landmarks (040) defined in a reference coordinate system to match a first set of anatomical landmarks (010) defined in a first coordinate system to a second set of anatomical landmarks (020) defined in a second coordinate system. Effectively, the third set of anatomical landmarks is used as an intermediary in obtaining the transformation between both input sets of coordinate systems. As the third set of anatomical landmarks includes the anatomical landmarks of both input sets, it is not needed for both input sets to be identical or even to overlap. Rather, even in case both input sets are entirely disjunct, i.e., not-overlapping, it is still possible to determine the transformation between the different coordinate systems.