Abstract: The invention relates to a method (100) for supervised training of an artificial neural network for medical image analysis. The method comprises acquiring (SI) first and second sets of training samples, wherein the training samples comprise feature vectors and associated predetermined labels, the feature vectors being indicative of medical images and the labels pertaining to anatomy detection, to semantic segmentation of medical images, to classification of medical images, to computer-aided diagnosis, to detection and/or localization of biomarkers or to quality assessment of medical images. The accuracy of predetermined labels may be better for the second set of training samples than for the first set of training samples. The neural network is trained (S3) by reducing a cost function, which comprises a first and a second part.

Abstract: The invention is related to a method for providing guidance data (30, 41) for positioning a region of interest (20, 31, 41, 56) of a subject (54), an X-ray source (52), and an X-ray detector (21, 34, 53). The method comprises: obtaining, by a processor (11), current positioning data of at least one palpable bony landmark (23, 33) derived from a palpation; obtaining current positioning data of the X-ray source (52) and of the X-ray detector (21, 34, 53) (S20); determining guidance data (30, 41) for positioning the region of interest (20, 31, 41, 56), the X-ray source (52) and the X-ray detector (21, 34, 53) and providing, by the processor (11), the guidance data (30·41) (S40).

Abstract: An X-ray imaging system (100) includes an X-ray source (110) and an X-ray detector (120) that are separated by an examination region (150) for performing an X-ray imaging operation on an object (160). A processor (140) is configured to identify (S120) one or more internal structures (180) within the object (160), based on a comparison of depth sensor data representing a three-dimensional surface (170) of the object (160), with an anatomical model comprising the one or more internal structures (180). The processor (140) is also configured to compute (S130), using the depth sensor data and the identified one or more internal structures (180), a surface projection (190) of the one or more internal structures, on the surface (170) of the object (160), from a perspective of the X-ray source (110); and to output (S140) an image representation of the surface projection (190) for displaying as an overlay on the surface (170) of the object (160).

Abstract: An apparatus (10) for generating a training set of anonymized images (40) for training an artificial intelligence (AI) component (42) from images (11) of a plurality of persons.

Abstract: An X-ray imaging system (100) includes an X-ray source (110), an X-ray detector (120), a depth camera (130), and a processor (140). The processor (140) receives depth camera image data from the depth camera, projects the depth camera image data onto a radiation-receiving surface of the X-ray detector (120), from a perspective of the X-ray source (110), and generates an image representation (170) of the projected depth camera image data on the radiation-receiving surface of the X-ray detector (120), from a perspective of the depth camera (130).

Abstract: A method of generating a modified X-ray image includes obtaining an X-ray image, obtaining a CT image corresponding to the X-ray image, determining a mapping between the X-ray image and the CT image, identifying a structure of interest in the CT image, generating an attenuation map from the CT image, the attenuation map indicative of attenuation due to the structure of interest or everything but the structure of interest on the X-ray image, and generating the modified X-ray image by subtracting the attenuation map from the X-ray image.

Abstract: A method is provided of obtaining body depth information for a patient who is lying on patient support. A patient support depth map of the upper surface of the patient support is obtained without the patient as well as an image and patient depth map of the patient on the patient support. Landmark body positions of the patient are extracted from the image so that points in a region of interest can be mapped to points of a template, using the identified landmark body positions. A body thickness is obtained for said points using the template, the depth value for the respective point and the patient support depth value for the respective point.

Abstract: The present invention relates to peripheral perfusion measurement. In order to provide more detailed peripheral perfusion characteristics for better knowledge about a current situation, a device (10) for peripheral perfusion measurement is provided that comprises an image data input (12), a data processor (14) and an output interface (16). The image data input receives at least one perfusion angiographic 2D X-ray image of a region of interest of a subject's foot and a 3D foot-model comprising spatial perfusion-related parameters. The data processor registers the 3D foot-model with the foot in the at least one perfusion angiographic X-ray image. The registering comprises a pose-estimation of the foot in the 2D X-ray image. The information is mapped between the 2D image and the 3D foot-model based on the pose-estimation. Image processing modification instructions are identified based on the mapped information.

Abstract: In order to enhance enhanced X-ray image inhalation quality monitoring, a metric is proposed hat reproducibly provides an index of ribs visible to be used in the assessment of the inhalation state. In an example, a detected diaphragm in a chest X-ray image may be projected into an atlas that contains labels for all intercostal spaces, namely spaces between rib centerlines. A spatial representation of both the clavicle and the ribs is provided in the atlas, a cumulative histogram is built for all points, i.e. pixels, of the diaphragm, for every point a rib label counter of the rib in the rib label map at that point is incremented as well as all ribs above it, the rib label counter is normalized by a division by the number of points, the median (or a different quantile) may be taken of this distribution serving as an inhalation index. An objective metric of inhalation state is thus achieved.

Abstract: A computer-implemented method for positioning a subject in medical imaging, comprising: receiving a first image (20) of a region of interest (14, 16) of the subject (S10); determining first positioning data based on the first image (20), wherein the first positioning data indicates an alignment of the region of interest (14, 16) relative to a first image acquisition unit used to acquire the first image (20) (S20); determining guidance data based on the first positioning data, wherein the guidance data comprises a guidance for an alignment of the region of interest (14, 16) relative to a second image acquisition unit used to acquire a second image (60) from a current alignment to a target alignment, wherein the target alignment is to correspond to that derived from the first positioning data (S30); providing the guidance data for acquiring the second image (60) (S40).

Abstract: A method for generating an image representation of slices through a body based on tomographic imaging data for the body. The method comprises processing reconstructed tomographic image slices to selectively embed in each slice image information from at least one 3D volume rendering of the slice plane within the 3D tomographic image dataset. This is done through a selection process wherein, based on a set of pre-defined criteria, a decision is made for each pixel in each reconstructed tomographic slice as to whether the pixel value should be replaced with a new, modified pixel value determined based on the at least one volume rendering. This may comprise simply swapping the pixel value for the value of the corresponding pixel value in the volume rendering, or it may comprise a more complex process, for instance blending the two values, or adjusting a transparency of the pixel value based on the at least one volume rendering.

Abstract: The disclosure relates to a system for analysis of medical image data, which represents a two-dimensional or three-dimensional medical image. The system is configured to read and/or determine, for the medical image, a plurality of image quality metrics and to determine a combined quality metrics based on the image quality metrics. The system is further configured so that the determination of the combined quality metrics takes into account an interaction between the image quality metrics in their combined effect on the combined quality metrics.

Abstract: A resource management system for better operation of a plurality of devices. The system comprises an input interface (IN) for receiving input data including one or more characteristics of at least one work object (P1-P3) and/or including context data. The at least one work object (P1-P3) can be processed by one or more processing devices (Mij). The said processing is specified in a respective work specification (S1-S3). A predictor component (PC) of the system is configured to predict, based on the input data, a change to the work specification. The system comprises an output interface (OUT) for providing output data that represents said predicted change.

Abstract: An apparatus (10) for generating a training set of anonymized images (40) for training an artificial intelligence (AI) component (42) from images (11) of a plurality of persons.

Abstract: The present invention relates to landmark and/or temporal event detection. It is proposed to utilize previously learned spatial statistical correlations between multiple landmarks in order to regularize convolutional neural networks (CNNs) either as a post-processing step or during training in order to utilize anatomical prior knowledge, reduce the false-positive prediction rate, and/or increase the accuracy and stability of the algorithm. The proposed apparatus and method may also be applied to improve the detection of correlated events in e.g., time-series by leveraging prior knowledge.

Abstract: The invention relates to a method (100) for supervised training of an artificial neural network for medical image analysis. The method comprises acquiring (SI) first and second sets of training samples, wherein the training samples comprise feature vectors and associated predetermined labels, the feature vectors being indicative of medical images and the labels pertaining to anatomy detection, to semantic segmentation of medical images, to classification of medical images, to computer-aided diagnosis, to detection and/or localization of biomarkers or to quality assessment of medical images. The accuracy of predetermined labels may be better for the second set of training samples than for the first set of training samples. The neural network is trained (S3) by reducing a cost function, which comprises a first and a second part.

Abstract: Method for assessing a position of a patient with respect to an automatic exposure control chamber, AEC chamber (11, 12), for a medical exam, wherein a patient is positioned between an X-ray source and the AEC chamber (11, 12); comprising the steps:—acquiring (S10) an X-ray image (32) of at least part of the patient, wherein the AEC chamber is configured for detecting a radiation dose of the X-ray source;—determining (S20), by the control unit, a position of the AEC chamber (11, 12) with respect to the patient from the acquired X-ray image (32);—determining (S30), by the control unit, an exam protocol performed on the patient dependent on the medical exam to be performed on the patient and determining, by the control unit, an ideal position of the AEC chamber (11, 12) with respect to the patient dependent on the exam protocol, wherein the ideal position relates to a position of the patient relative to the AEC chamber (11, 12), in which the detected radiation dose is reliable for the medical exam; and—determin

Abstract: The field of view of an X-ray imaging system should be set appropriately to ensure that anatomical information of interest is not omitted. In particular, it is necessary to ensure that the operator of an X-ray system does not allow a patient to leave the X-ray imaging system until it is certain that the correct anatomy has been imaged. This application discusses a technique enable the visualization of a field of view boundary error caused by the incorrect configuration of an X-ray imaging system. Optionally, the boundary error is displayed either on a user display of a system console, or by projecting the field of view error onto the patient in the X-ray system. Thus, an operator of the system may be alerted to the presence of a boundary error, enabling a new X-ray exposure to be taken, if necessary.

Abstract: The present invention relates to an apparatus (10) for determining an orientation of a patients chest. The apparatus comprises: an input unit (20); and a processing unit (30). The input unit is configured to receive an image of a patient, the image comprising image data of the patients chest. The input unit is configured to receive an X-ray radiograph of the patient's chest acquired by an X-ray imaging unit with an X-ray imaging axis extending from an X-ray source to an X-ray detector. The input unit is configured to provide the image and the X-ray radiograph to the processing unit. The processing unit is configured to determine an orientation of the patients chest in the X-ray radiograph with respect to the X-ray imaging axis, the determination comprising utilization of the image and the X-ray radiograph.

Abstract: An imaging system (SYS), comprising a medical imaging apparatus (IA). The medical imaging apparatus comprises a detector (D) for acquiring a first image of a patient in an imaging session, and a display unit (DD) for displaying the first image on a screen. The system further comprises, distinct from the medical imaging apparatus (IA), a mobile image processing device (MIP). The mobile processing device (MIP) comprises an interface (IN) for receiving a representation of the first image, and an image analyzer (IAZ) configured to analyze the representation and, based on the analysis, to compute, during the imaging session, medical decision support information. The decision support information is displayed on an on-board display device (MD) of the mobile processing device (MIP).