Abstract: The present disclosure provides a medical system (100) comprising at least one data processing unit (111, 121, 130), a mechanical ventilator (110), configured to be operated to cause a ventilation status at a to-be-ventilated subject, and a medical imaging device (120), configured as a mobile device. The at least one data processing unit (111, 121, 130) is configured to obtain, from the mechanical ventilator (110), ventilation status information associated with the ventilation status of the to-be-ventilated subject. Further, the at least one data processing unit (111, 121, 130) is further configured to trigger acquisition of a first image if the ventilation status meets a first image acquisition criterion. Furthermore, the at least data processing unit (111, 121, 130) is further configured to trigger acquisition of at least one subsequent, further image if the ventilation status meets a second image acquisition criterion.

Abstract: There is provided a computer-implemented method of estimating lung volume from radiographic images. The method comprises: registering (102) a two dimensional radiographic image (12) of a patients chest to a three dimensional radiographic image (14) of the patients chest to estimate data describing projection geometry (16) of an imaging setup used to capture the two dimensional radiographic image; using the projection geometry to estimate (104) at least one radiographic magnification factor (18) relating to the imaging setup; and calculating (106) an estimated lung volume (20) using the two dimensional radiographic image and the at least one radiographic magnification factor.

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 system and related method for image processing. The system comprises an input (IN) interface for receiving two segmentation maps for an input image. The two segmentation maps (11,12) obtained by respective segmentors, a first segmentor (SEG1) and a second segmentor (SEG2). The first segmentor (SEG1) implements a shape-prior-based segmentation algorithm. The second segmentor (SEG2) implements a segmentation algorithm that is not based on a shape-prior, or at least the second segmentor (SEG2) accounts for one or more shape priors at a lower weight as compared to the first segmentor (SEG1). A differentiator (DIF) configured to ascertain a difference between the two segmentation maps. The system may allow detection of abnormalities.

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: Provided is an apparatus (10) for processing dark-field X-ray image data information. The apparatus comprises a data interface (20); and a data processing unit (30). The data interface (20) is configured to provide the data processing unit with dark-field X-ray image data. The data processing unit (30) is configured to segment the image data to provide segmented image data of the lung, the segmented image data comprising at least a first lung segment and a second lung segment distinguishable therefrom, the first lung segment and the second lung segment being segmented to have a defined volume. The data processing unit (30) is further configured to derive, from the image data, at least a first dark-field signal value assigned to the first lung segment and a second dark-field signal value assigned to the second lung segment.

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: Presented are concepts for initialising a model for model-based segmentation of an image which use specific landmarks (e.g. detected using other techniques) to initialize the segmentation mesh. Using such an approach, embodiments need not be limited to predefined model transformations, but can initialise a segmentation mesh with arbitrary shape. In this way, embodiments may provide for an image segmentation algorithm that not only delivers a robust surface-based segmentation result but also does so for strongly varying target structure variations (in terms of shape).

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: Multi-task deep learning method for a neural network for automatic pathology detection, comprising the steps: receiving first image data (I) for a first image recognition task; receiving (S2) second image data (V) for a second image recognition task; wherein the first image data (I) is of a first datatype and the second image data (V) is of a second datatype, different from the first datatype; determining (S3) first labeled image data (IL) by labeling the first image data (I) and determining second synthesized labeled image data (ISL) by synthesizing and labeling the second image data (V); training (S4) the neural network based on the received first image data (I), the received second image data (V), the determined first labeled image data (IL) and the determined second labeled synthesized image data (ISL); wherein the first image recognition task and the second image recognition task relate to a same anatomic region where the respective image data is taken from and/or relate to a same pathology to be recogni

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).

Abstract: Medical radiography requires specialist control of radiography equipment to achieve good imaging results. Typical errors that can occur consist of an inappropriate field of view being accidentally applied. This results in a “cropping effect” in which portions of the region of interest of a patient which would be of clinical use are omitted from the image. Conventionally, the only solution is to re-take the entire image with a more appropriate (and inevitably larger) field of view selected. This is undesirable, because it might require recall of the patient from another location, and the patient will be subject to two exposures, thus undesirably increasing their X-ray dosage. The present application proposes to use an anatomical atlas to analyse an X-ray image output from an initial exposure, in particular to assess whether significant anatomical elements are missing from the image.

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: An image processing system and related method. The system comprises an input interface (IN) configured for receiving an n[?2]-dimensional input image with a set of anchor points defined in same, said set of anchor points forming an input constellation. A constellation modifier (CM) is configured to modify said input constellation into a modified constellation. A constellation evaluator (CE) configured to evaluate said input constellation based on said hyper-surface to produce a score. A comparator (COMP) is configured to compare said score against a quality criterion. Through an output interface (OUT) said constellation is output if the score meets said criterion. The constellation suitable to define a segmentation for said input image.

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: An image processing system and related method. The system comprises an input interface (IN) configured for receiving an n[?2]-dimensional input image with a set of anchor points defined in same, said set of anchor points forming an input constellation. A constellation modifier (CM) is configured to modify said input constellation into a modified constellation. A constellation evaluator (CE) configured to evaluate said input constellation based on said hyper-surface to produce a score. A comparator (COMP) is configured to compare said score against a quality criterion. Through an output interface (OUT) said constellation is output if the score meets said criterion. The constellation suitable to define a segmentation for said input image.