Abstract: The present invention relates to a device and method for segmenting the membranous septum. The method comprises segmenting the left ventricular outflow tract, LVOT, of the heart in a 3D image data set; determining wall thickness information indicative of the wall thickness of the septum at different locations of a part of the segmented LVOT that is oriented towards the right heart chambers and right atrium; mapping the determined wall thickness information onto the surface of said part of the segmented LVOT; and segmenting the membranous septum in the 3D image data set based on the mapped wall thickness information.

Abstract: A computer implemented method of making a measurement associated with a feature of interest in an image. The method comprises using (302) a model trained using a machine learning process to take the image as input and predict a pair of points between which to make the measurement of the feature of interest in the image. The method then comprises determining (304) the measurement, based on the predicted pair of points.

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 method and system for generating predictive indicators of likely pathologies. Spectral image data is processed to identify regions of interest, which represent different sets of one or more organs. Each region of interest is then processed using a respective set of one or more machine-learning algorithms to produce a respective number of predictive indicators for each region of interest. At least one of these predictive indicators is/are then output.

Abstract: In a method of segmenting a tubular feature in an image, a sequence of overlapping portions of the image are segmented using a trained model. The overlapping portions are positioned along the length of the tubular feature and combined to determine a segmentation of the tubular feature.

Abstract: In a conventional system for preparing reports on findings in medical images, the actual formulation of the report is not or only insufficiently supported by the computer-based system. Although there are efforts to improve such a system through automatic reporting, however, in previous systems, the error rate is too high and/or the operation of the system too complicated. This application proposes to provide text prediction to a user on the display device. The text prediction is based on prior analyzing the image content of a medical image at is displayed to the user at least when the user activates a text field shown on the display device via the input unit. The displayed text prediction is selected from a pre-defined set of text modules that are associated to the analysis result.

Abstract: According to an aspect, there is provided a computer implemented method of processing an image of a subject comprising lymph nodes, the method comprising: segmenting lymph nodes in image data corresponding to the image, delineating lymph nodes from the segmented image data, classifying a lymph node as belonging to a predefined region, evaluating a region of the image based on an assessment of the risk of the region comprising a malign lymph node, and indicating the evaluation of the region.

Abstract: System (OGS) and related method for generating an objective function for use in radiation treatment, RT, planning. The system may include a machine learning model (M) and a training system (TS) for training the model (M) based on training data. The training data may include previous (at least partial) RT plans, and a user awarded ranking thereof. The model, once trained, may be used as the objective function. The system allows a user to turn, in a defined manner, clinical objectives or goals into a computable objective function which can be used for RT planning.

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 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 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: 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: The invention relates to a system for assessing a pulmonary image which allows for an improved assessment with respect to lung nodules detectability. The pulmonary image is smoothed for providing different pulmonary images (20, 21, 22) with different degrees of smoothing, wherein signal values and noise values, which are indicative of the lung vessel detectability and the noise in these images, are determined and used for determining an image quality being indicative of the usability of the pulmonary image to be assessed for detecting lung nodules. Since a pulmonary image shows lung vessels with many different vessel sizes and with many different image values, which cover the respective ranges of potential lung nodules generally very well, the image quality determination based on the different pulmonary images with different degrees of smoothing allows for a reliable assessment of the pulmonary image's usability for detecting lung nodules.

Abstract: A computer implemented method of making a measurement associated with a feature of interest in an image. The method comprises using (302) a model trained using a machine learning process to take the image as input and predict a pair of points between which to make the measurement of the feature of interest in the image. The method then comprises determining (304) the measurement, based on the predicted pair of points.

Abstract: The present disclosure relates to a method for medical imaging method for locating anatomical landmarks of a predetermining defined anatomy.

Abstract: In a method of segmenting a tubular feature in an image, a sequence of overlapping portions of the image are segmented using a trained model. The overlapping portions are positioned along the length of the tubular feature and combined to determine a segmentation of the tubular feature.

Abstract: The invention relates to a segmentation system for segmenting an object in an image. The segmentation system is configured to place a surface model comprising surface elements within the image, to determine for each surface element a respective sub volume (6) of the image and to use a neural network (51) for determining respective distances between the surface elements and the boundary of the object in the image based on the determined subvolumes. The surface model is then adapted in accordance with the determined distances, in order to segment the object. This segmentation, which is based on the subvolumes of the image and the neural network, is improved in comparison to known techniques which rely, (10) for instance, on a sampling of candidate points along lines being perpendicular to the respective surface element and on a determination of likelihoods for the candidate points that they indicate a boundary of the object.

Abstract: There is provided a computer-implemented method and system (100) for determining regions of hyperdense lung parenchyma in an image of a lung. The system (100) comprises a memory (106) comprising instruction data representing a set of instructions and a processor (102) configured to communicate with the memory and to execute the set of instructions. The set of instructions, when executed by the processor (102), cause the processor (102) to locate a vessel in the image, determine a density of lung parenchyma in a region of the image that neighbours the located vessel, and determine whether the region of the image comprises hyperdense lung parenchyma based on the determined density, hyperdense lung parenchyma having a density greater than ?800 HU.

Abstract: The invention relates to a magnetic resonance imaging data processing system (126) for processing motion artifacts in magnetic resonance imaging data sets using a deep learning network (146, 502, 702) trained for the processing of motion artifacts in magnetic resonance imaging data sets. The magnetic resonance imaging data processing system (126) comprises a memory (134, 136) storing machine executable instructions (161, 164) and the trained deep learning network (146, 502, 702). Furthermore, the magnetic resonance imaging data processing system (126) comprises a processor (130) for controlling the magnetic resonance imaging data processing system.

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.