Abstract: Presented are concepts for obtaining a confidence measure for a machine learning model. One such concept process input data with the machine learning model to generate a primary result. It also generate a plurality of modified instances of the input data and processes the plurality of modified instances of the input data with the machine learning model to generate a respective plurality of secondary results. A confidence measure relating to the primary result is determined based on the secondary results.

Abstract: The present invention relates to edge restoration. In order to improve a restoration of the artificially created cleansed edges, an apparatus is proposed to automatically restore image edges after digital subtraction of digital material substitution to optimally resemble image edges in unmodified locations. The appearance of edges is machine-learned in an unsupervised non-analytical way from unmodified locations, and then, after digital suppression or digital material substitution, applied to the artificially created cleansed edges.

Abstract: A method and system are provided for reconstructing a motion-compensated nuclear image of a subject, as well as an arrangement for method. The reconstruction method comprises receiving nuclear image data the acquiring a nuclear image, and a computer program for carrying out the for multiple motion states, reconstructing the data into an image for each motion state, and calculating a deformation vector field for each state for mapping the image onto a reference motion state. Calculating the deformation vector field comprises providing an initial vector field, defining at least one rigid region of the subject, incorporating that rigid region into the initial vector field, and calculating the deformation vector field with the incorporated rigid region. The method further comprises mapping the reconstructed image of each motion state onto the reference state using the deformation vector fields; and combining the mapped images into a motion-compensated nuclear image.

Abstract: Presented are concepts for obtaining a confidence measure for a machine learning model. One such concept process input data with the machine learning model to generate a primary result. It also generate a plurality of modified instances of the input data and processes the plurality of modified instances of the input data with the machine learning model to generate a respective plurality of secondary results. A confidence measure relating to the primary result is determined based on the secondary results.

Abstract: The present invention relates to the field of medical ultrasound imaging, and in particular to a medical ultrasound image processing device for supporting reproducible acquisition of 2D ultrasound images.

Abstract: A radiology workstation (20) includes a display device (22), at least one user input device (24, 26, 28), and an electronic data processor. The workstation provides an image rendering component (30) for rendering a radiological image, and a report data entry component (32) for entry of a radiology examination report (34) via the at least one user input device and for display of the radiology examination report during entry. The workstation further provides a dynamic checklist component (40) for storing a queue (44) of open checklist items, and for: displaying a checklist comprising open checklist items stored in the queue; dynamically analyzing the radiology examination report during entry to identify a checklist update trigger; updating the queue of open checklist items based on the identified checklist update trigger; and updating the display of the checklist to comprise the open checklist items stored in the updated queue.

Abstract: There is provided a method and apparatus for adjusting a model of an anatomical structure in a sequence of images of the anatomical structure. The model is placed with respect to the anatomical structure in the sequence of images. A user input is received to adjust the model in a selected image of the sequence, and based on the user input, a part of the model that lies in the selected image and a previously unadjusted part of the model that lies in one or more other images of the sequence is adjusted, whilst fixing in place a previously adjusted part of the model that lies in other images of the sequence.

Abstract: The invention relates to a device for processing CT imaging data, comprising a processing unit, which is configured to receive a plurality of sets of CT imaging data recorded at different imaging positions and at different points in time. Furthermore, the processing device is configured to provide a plurality of auxiliary sets of CT imaging data, each auxiliary set of CT imaging data comprising processed image data allocated to spatial positions inside a respective spatial section of the object space, wherein a given one of the spatial sections contains those spatial positions which are covered by those sets of CT imaging data acquired at a respective one of the imaging positions, and to generate the processed image data for a given spatial position using those of the sets of CT imaging data acquired at the respective one of the imaging positions.

Abstract: There is provided a method and apparatus for adjusting a model of an anatomical structure in a sequence of images of the anatomical structure. The model is placed with respect to the anatomical structure in the sequence of images. A user input is received to adjust the model in a selected image of the sequence, and based on the user input, a part of the model that lies in the selected image and a previously unadjusted part of the model that lies in one or more other images of the sequence is adjusted, whilst fixing in place a previously adjusted part of the model that lies in other images of the sequence.

Abstract: A method generates and uses a correspondence probability map for visualization of two image datasets. The method includes obtaining two image datasets and obtaining an image registration algorithm that includes a correspondence model. The method further includes registering the two image datasets to generate a displacement vector field and generating a correspondence probability map, using the correspondence model, based on the two image datasets. The method further includes using the correspondence probability map to visualize the two image datasets. A computing system (120) includes a memory device (124) configured to store instructions, including a visualization module (130), and a processor (122) that executes the instructions, which causes the processor to generate and employ a correspondence probability map for visualization of two image datasets.

Abstract: The present invention relates to a device and method for registration of two images. To increase the accuracy of the registration the device comprises an input unit (126) for receiving one or more pairs of negative landmarks, which are indicated in the two images, a pair comprising two negative landmarks, one in each image, which structures are not identical and should not be aligned by the registration.

Abstract: The present invention relates to the field of medical ultrasound imaging, and in particular to a medical ultrasound image processing device for supporting reproducible acquisition of 2D ultrasound images.

Abstract: A radiology workstation (20) includes a display device (22), at least one user input device (24, 26, 28), and an electronic data processor. The workstation provides an image rendering component (30) for rendering a radiological image, and a report data entry component (32) for entry of a radiology examination report (34) via the at least one user input device and for display of the radiology examination report during entry. The workstation further provides a dynamic checklist component (40) for storing a queue (44) of open checklist items, and for: displaying a checklist comprising open checklist items stored in the queue; dynamically analyzing the radiology examination report during entry to identify a checklist update trigger; updating the queue of open checklist items based on the identified checklist update trigger; and updating the display of the checklist to comprise the open checklist items stored in the updated queue.

Abstract: The present invention relates to a device and method for registration of two images. To increase the accuracy of the registration the device comprises an input unit (126) for receiving one or more pairs of negative landmarks, which are indicated in the two images, a pair comprising two negative landmarks, one in each image, which structures are not identical and should not be aligned by the registration.

Abstract: The invention relates to a device for processing CT imaging data, comprising a processing unit, which is configured to receive a plurality of sets of CT imaging data recorded at different imaging positions and at different points in time. Furthermore, the processing device is configured to provide a plurality of auxiliary sets of CT imaging data, each auxiliary set of CT imaging data comprising processed image data allocated to spatial positions inside a respective spatial section of the object space, wherein a given one of the spatial sections contains those spatial positions which are covered by those sets of CT imaging data acquired at a respective one of the imaging positions, and to generate the processed image data for a given spatial position using those of the sets of CT imaging data acquired at the respective one of the imaging positions.

Abstract: A device (1) for determining image quality of a radiogram image includes: an acquiring module (10), which is configured to acquire the radiogram image; a segmentation module (20), which is configured to segment a thoracic bone structure on the acquired radiogram image into segmented thoracic bone structure contours and a tissue structure on the acquired radiogram image into segmented tissue structure contours; and a quality-determination module (30), which is configured to determine a image quality coefficient for the acquired radiogram image based on a comparison of a first position of the segmented thoracic bone structure contours with a second position of the segmented tissue structure contours.

Abstract: A method generates and uses a correspondence probability map for visualization of two image datasets. The method includes obtaining two image datasets and obtaining an image registration algorithm that includes a correspondence model. The method further includes registering the two image datasets to generate a displacement vector field and generating a correspondence probability map, using the correspondence model, based on the two image datasets. The method further includes using the correspondence probability map to visualize the two image datasets. A computing system (120) includes a memory device (124) configured to store instructions, including a visualization module (130), and a processor (122) that executes the instructions, which causes the processor to generate and employ a correspondence probability map for visualization of two image datasets.

Abstract: The present invention relates to a device (1) for determining image quality of a radiogram image, the device comprising: an acquiring module (10), which is configured to acquire the radiogram image; a segmentation module (20), which is configured to segment a thoracic bone structure on the acquired radiogram image into segmented thoracic bone structure contours and a tissue structure on the acquired radiogram image into segmented tissue structure contours; and a quality-determination module (30), which is configured to determine a image quality coefficient for the acquired radiogram image based on a comparison of a first position of the segmented thoracic bone structure contours with a second position of the segmented tissue structure contours.