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: 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 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 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.

Abstract: The present invention relates to an apparatus for the detection of opacities in X-ray images. It is described to provide (210) an analysis X-ray image of a region of interest of an analyzed body part. A model of a normal region of interest is provided (220), wherein the model is based on a plurality of X-ray images of the region of interest. At least one abnormality is detected (230) in the region of interest of the analyzed body part, the detection comprising comparing the analysis X-ray image of the region of interest and the model of the normal region of interest. Information is output (240) on the at least one abnormality.

Abstract: The present invention relates to a device (1) for determining positioning data for an X-ray image acquisition device (2) on a mobile patient support unit (3), the device comprising: a processing unit (10); and a status detector (11); wherein the status detector (11) is configured to acquire geometry data and type data of a mobile patient support unit (3) and of an X-ray image acquisition device (2) on the mobile patient support unit (3), and to transmit a status signal comprising the geometry data and the type data; wherein the processing unit (10) is configured to receive the status signal and data about region of interest position on the patient; and wherein, based on the status signal and the region of interest position, the processing unit (10) is configured to determine positioning and alignment data for an X-ray image acquisition device (2) on the mobile patient support unit (3), and to provide an image acquisition protocol for the X-ray image acquisition device (2).

Abstract: The invention relates to an apparatus for identifying a candidate object in image data and determining a likelihood that the candidate object is an object from an object class. The apparatus comprises an image data receiving unit for receiving image data of an object of the object class, a seed element selecting unit for selecting a portion of the image elements as seed elements, a contour point identifying unit for identifying, for each seed element (SE), contour points, the contour points of a seed element circumscribing a candidate object which comprises the seed element, and a seed score determining unit for determining, for each seed element, a seed score indicative of a likelihood that the candidate object is an object from the object class. The invention allows differentiation between an object of an object class of interest and artifacts.

Abstract: The invention relates to an image processing device (10) comprising a data input (11) for receiving a 3D diagnostic image and a segmentation unit (12) for segmenting a thoracic cavity, a pelvic cavity, an abdominopelvic cavity or a combination of a thoracic cavity and an abdominopelvic cavity in the 3D diagnostic image and for determining a boundary surface thereof. The device also comprise a surface texture processor (13) for determining a surface texture for the boundary surface by projecting image information from a local neighborhood of the boundary surface in the 3D diagnostic image onto the boundary surface. The device comprises an output (14) for outputting a visual representation of at least one flat anatomical structure, comprising one or more ribs, a sternum, one or more vertebrae and/or a pelvic bone complex, adjacent to the body cavity by applying and visualizing the surface texture on the boundary surface.

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: 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: The present invention relates to a mobile X-ray device, comprising: a housing with an X-ray source arranged therein, a sensor system comprising one or more sensors for aligning the X-ray source to an object to be scanned and/or detecting at least one extrinsic object in a predefined area in a vicinity of the X-ray beam of the X-ray source; a processor unit comprising determining the alignment of the X-ray source and the object to be scanned based on signals received from the sensor system; image acquisition for generating an X-ray image; activating a blockage signal for the mobile X-ray device based on signals received from the sensor system; and an interface for outputting the generated X-ray image and/or information; wherein image acquisition is blocked, if the sensor system signals one of the following: at least one extrinsic object is detected within the predefined area; lack of alignment between the X-ray source and the object to be scanned.