A vehicle control device includes a tracking unit estimating a motion of a moving object, a model selection unit selecting a motion model corresponding to a moving object type, an abnormality determination unit determining a presence or absence of an abnormality of the estimation of the motion of the moving object based on the estimated moving object motion and the motion indicated by the motion model, and a control unit. A control mode in which the control unit controls traveling of a host vehicle when the abnormality determination unit determines that the abnormality is present differs from a control mode in which the control unit controls the traveling of the host vehicle when the abnormality determination unit determines that the abnormality is absent.

A method comprises receiving images representing manipulating cellular bodies that includes exerting force pulses to the bodies on a wall surface; analyzing the images to determine the size of the bodies and tracking locations during and after each of force pulses, the tracking locations defining first trajectories of the bodies moving away from the wall surface and trajectories of the bodies moving towards the wall surface; determining densities of the bodies using the second trajectories and a sedimentation model of the bodies moving towards the wall surface and determining body velocities based on the first trajectories and a velocity model of the bodies moving away from the wall surface; and, determining a contrast factor for each body based on the sizes and the densities, the force applied to the bodies and the body velocities and determining a compressibility for each of the bodies based on the determined contrast factors.

A method or system for assessing a patient response to a cancer treatment is provided. The method or system includes acquiring at least one base-line radiological image related to a patient immediately before a treatment, acquiring at least one follow-up radiological image during or after the treatment at a predetermined time interval, estimating a first number of specific tumor cells in a region of interest of the patient based on image features of the base-line radiological image using an algorithm or a model, estimating a second number of specific tumor cells in the region of interest of the patient based on image features of the follow-up radiological image using the algorithm or the model, obtaining a difference between the first number of specific tumor cells and the second number of specific tumor cells, and classifying a treatment response to a cancer based on the difference.

A computer-implemented method is for classifying a lesion. In an embodiment, the method includes receiving a first medical image of an examination volume, the first medical image corresponding to a first examination time; receiving a second medical image of the examination volume, the second medical image corresponding to a second examination time, different from the first examination time; determining a first lesion area corresponding to a lesion within the first medical image; determining a registration function based on a comparison of the first medical image and the second medical image; determining a second lesion area within the second medical image based on the registration function and the first lesion area; and classifying the lesion within the first medical image based on the second lesion area. A computer-implemented method for providing a trained classification function, a classification system, and computer program products and computer-readable media are also disclosed.

Various systems, methods, and devices for identifying intraoperative bleeding are described. An example method includes identifying a first frame depicting a surgical scene; identifying a second frame depicting the surgical scene; identifying whether the second frame depicts bleeding by analyzing the first frame and the second frame; and outputting the second frame with an augmentation indicating whether bleeding is depicted in the second frame.

A dynamic image analysis apparatus includes a hardware processor and a display. The hardware processor performs tagging for classifying. The hardware processor extracts a feature amount from frame images and calculates a change amount in a time direction of the feature amount. When a second dynamic image is input, the hardware processor selects an extractable feature amount from a related dynamic image which is the first dynamic image determined to be related with the second dynamic image, the first dynamic image and the second dynamic image. The display is capable of displaying a change over time in the frame images included in one occasion of imaging of the dynamic image, the change over time between the second dynamic image and the selected related dynamic image and the change over time between the feature amount extracted from the second dynamic image and the feature amount extracted from the related image.

The present disclosure relates to a computer-implemented method for providing feedback during scanning of a dental object, comprising the steps of obtaining scan data of the dental object; generating a digital three-dimensional (3D) representation of the dental object based on the scan data; obtaining new scan data of the dental object; generating an updated digital 3D representation by adding the new scan data to the digital 3D representation; determining added information to the digital 3D representation by comparing the new scan data to at least one of the digital 3D representation(s) and/or by comparing the updated digital 3D representation to the digital 3D representation(s); and providing a feedback signal; wherein the feedback signal correlates with the added information and/or a determined overlap. The present disclosure further relates to a 3D scanner system and a handheld intraoral scanning device, configured for providing the feedback signal such as by executing the computer-implemented method.

The present application relates to an automated segmentation method for use with echocardiograms (echo). The method uses an iterative Dijkstra's algorithm, a strategic node selection, and a novel cost matrix formulation based on intensity peak prominence and is this termed the “Prominence Iterative Dijkstra's” algorithm, or ProID.

The present disclosure provides a method for treating clinical or pre-clinical skin damage in a skin field of a subject, wherein the skin field has been allocated a skin cancerization field index (SCFI) score of at least 1 as determined by a process comprising the steps of: (i) assessing the number of keratoses in the skin field; (ii) assessing the thickness of the thickest keratosis in the skin field; and (iii) assessing the proportion of the field affected by clinical or subclinical skin damage. Based on the assessments made in (i), (ii) and (iii) the subject is optionally treated by at least one of (a) freezing one or more lesions, (b) shaving, curetting or surgically removing one or more lesions, (c) applying a topical treatment for actinic keratosis, basal cell carcinoma or squamous cell carcinoma, and (d) radiation therapy.

Provided are a medical image processing apparatus, a medical image processing method, and a medical image processing program by which it is possible to automatically perform time measurement related to an examination state of time-series medical images. In a medical image processing apparatus including a processor, the processor is configured to perform: medical image acquisition processing for sequentially acquiring time-series medical images; examination state recognition processing for recognizing an examination state on the basis of the sequentially acquired medical images; time measurement processing for performing time measurement related to the examination state; time measurement control processing for controlling a behavior of the time measurement in accordance with a recognition result of the examination state; and report processing for causing a report unit to report information on a state of the time measurement to a user.