Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: A computer-implemented method is for operating a medical imaging system. The imaging system includes an imaging unit and a positioning apparatus for positioning an examination object. In an embodiment, the method includes ascertainment of object information relating to the examination object; determination of a region of interest for the examination object, the region of interest being defined as a region of the examination object to be examined by the imaging unit in the context of an examination to be performed; arrival at a determination concerning a positional state of the examination object relative to the positioning apparatus and, to be precise, based on the region of interest and the object information; and provision of user output for a user of the imaging system based on the determination.

Abstract: A driver assistance system for motor vehicles. The system includes a vehicle surroundings sensor system (for detecting vehicle surroundings information which includes multiple sub-sensor systems, in a driving mode for hands-free driving, a vehicle guidance being carried out based on vehicle surroundings information detected by the vehicle surroundings sensor system, the vehicle guidance encompassing functions for the vehicle transverse guidance and for the vehicle longitudinal guidance. An error handling unit is configured, in the driving mode for hands-free driving, to maintain the vehicle guidance with different restrictions of functions of the vehicle guidance, and to output a vehicle guidance take-over request to a person driving the vehicle, in different failure scenarios of sub-sensor systems of the vehicle surroundings sensor system.

Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: Patient biographic data may be estimated by receiving patient image data, applying the patient image data to a machine learned model, the machine learned model trained on second patient data and trained to map the second patient data to associated biographic data using machine learned features, generating the patient biographic data based on the applying and the machine learned features, and outputting the patient biographic data. The patient biographic data may include a patient weight, a patient height, a patient gender, and a patient age.

Abstract: The method comprises receiving an image sequence of the subject from the camera during the medical imaging scan; receiving at least one of the current position or velocity of the patient table during the medical imaging scan; performing a motion tracking analysis of the image sequence to extract a motion model, wherein at least one of the motion tracking analysis or the motion model is tailored to the body region of interest and takes into account the at least one of the current patient table position or velocity; and analysing the motion model to detect subject motion and, if the detected motion is above a threshold, at least one of adapting the medical imaging examination or issuing an alert.

Abstract: The present disclosure provides a method, apparatus and computer program product for suppressing noise in 3D road surface reconstruction. The method includes: acquiring an image related to a road surface; extracting horizontal line information and ROI (region of interest) information from the image, wherein the horizontal line information comprises a horizontal line pixel value, and the ROI information is used to perform 3D road surface reconstruction for the road surface; judging whether the difference between the horizontal line pixel value and a preset horizontal line pixel value is greater than a pixel threshold; judging whether the horizontal line pixel value is greater than a compensation threshold when the difference is greater than the pixel threshold; determining a compensation value based on the difference when the horizontal line pixel value is not greater than the compensation threshold; and adjusting the ROI information based on the compensation value.

Abstract: For training for and performance of patient modeling from surface data in a medical system, a progressive multi-task model is used. Different tasks for scanning are provided, such as landmark estimation and patient pose estimation. One or more features learned for one task are used as fixed or constant features in the other task. This progressive approach based on shared features increases efficiency while avoiding reductions in accuracy for any given task.

Abstract: For patient model estimation from surface data in a medical system, a stream or sequence of depth camera captures are performed. The fitting of the patient model is divided between different times or parts of the sequence, using the streaming capture to distribute processing and account for patient movement. Less manual involvement may be needed due to the regular availability of image captures. Subsequent fitting may benefit from previous fitting.

Abstract: The method comprises receiving an image sequence of the subject from the camera during the medical imaging scan; receiving at least one of the current position or velocity of the patient table during the medical imaging scan; performing a motion tracking analysis of the image sequence to extract a motion model, wherein at least one of the motion tracking analysis or the motion model is tailored to the body region of interest and takes into account the at least one of the current patient table position or velocity; and analysing the motion model to detect subject motion and, if the detected motion is above a threshold, at least one of adapting the medical imaging examination or issuing an alert.

Abstract: A driver assistance system for motor vehicles. The system includes a vehicle surroundings sensor system (for detecting vehicle surroundings information which includes multiple sub-sensor systems, in a driving mode for hands-free driving, a vehicle guidance being carried out based on vehicle surroundings information detected by the vehicle surroundings sensor system, the vehicle guidance encompassing functions for the vehicle transverse guidance and for the vehicle longitudinal guidance. An error handling unit is configured, in the driving mode for hands-free driving, to maintain the vehicle guidance with different restrictions of functions of the vehicle guidance, and to output a vehicle guidance take-over request to a person driving the vehicle, in different failure scenarios of sub-sensor systems of the vehicle surroundings sensor system.

Abstract: A framework for sensor-based patient treatment support. In accordance with one aspect, one or more sensors are used to acquire sensor data of one or more objects of interest. The sensor data is then automatically interpreted to generate processing results. One or more actions may be triggered based on the processing results to support treatment of a patient, including supporting medical scanning of the patient.

Abstract: A computer-implemented method is for operating a medical imaging system. The imaging system includes an imaging unit and a positioning apparatus for positioning an examination object. In an embodiment, the method includes ascertainment of object information relating to the examination object; determination of a region of interest for the examination object, the region of interest being defined as a region of the examination object to be examined by the imaging unit in the context of an examination to be performed; arrival at a determination concerning a positional state of the examination object relative to the positioning apparatus and, to be precise, based on the region of interest and the object information; and provision of user output for a user of the imaging system based on the determination.

Abstract: Patient biographic data may be estimated by receiving patient image data, applying the patient image data to a machine learned model, the machine learned model trained on second patient data and trained to map the second patient data to associated biographic data using machine learned features, generating the patient biographic data based on the applying and the machine learned features, and outputting the patient biographic data. The patient biographic data may include a patient weight, a patient height, a patient gender, and a patient age.

Abstract: For patient model estimation from surface data in a medical system, a stream or sequence of depth camera captures are performed. The fitting of the patient model is divided between different times or parts of the sequence, using the streaming capture to distribute processing and account for patient movement. Less manual involvement may be needed due to the regular availability of image captures. Subsequent fitting may benefit from previous fitting.

Abstract: A method is for determining a patient weight and/or a body mass index of a patient. The method includes acquiring image data containing depth information of the patient; generating a surface model of the patient based upon the image data acquired; determining density information or X-ray attenuation information of at least part of the patient; and determining at least one of the total patient weight and the body mass index of the patient using the surface model generated and the density information or X-ray attenuation information determined.

Abstract: In an embodiment, a method includes providing a three-dimensional structure image including surface data relating to external contours and structure data relating to internal structures of a body region; recording the body region via a camera system, to produce a recording while a patient is situated in or on the medical imaging system; registering, at least locally and as a structure image, the three-dimensional surface image in relation to the three-dimensional structure image; producing an overlay image including at least part of the structure image registered including structure data of the three-dimensional structure image in a form of an overlay with at least one of the three-dimensional surface image and a real view of a corresponding part of the body region of the patient; and controlling the medical imaging system to record body regions of the patient based upon an arrangement of the structure data registered in the overlay image.

Abstract: A framework for sensor-based patient treatment support. In accordance with one aspect, one or more sensors are used to acquire sensor data of one or more objects of interest. The sensor data is then automatically interpreted to generate processing results. One or more actions may be triggered based on the processing results to support treatment of a patient, including supporting medical scanning of the patient.

Abstract: For training for and performance of patient modeling from surface data in a medical system, a progressive multi-task model is used. Different tasks for scanning are provided, such as landmark estimation and patient pose estimation. One or more features learned for one task are used as fixed or constant features in the other task. This progressive approach based on shared features increases efficiency while avoiding reductions in accuracy for any given task.