The A method of augmenting a view of a real-world environment with a view of a volumetric video object on a user device is disclosed . The method includes determining a current pose information (CPI) indicating a current pose of the view of the real-world environment and a desired pose of the volumetric video object in the real-world environment. The method further includes sending the CPI to a remote server. The method further includes receiving a rendered view of the volumetric video object that has been rendered in accordance with the CPI from the remote server. The method also includes augmenting the view of the real-world environment by at least mapping the rendered view of the volumetric video object onto a planar mapping surface arranged according to the desired position of the volumetric video object.

Disclosed are method and apparatus to recognize actors during normal system operation. The method includes defining actor input such as hand gestures, executing and detecting input, and identifying salient features of the actor therein. A model is defined from salient features, and a data set of salient features and/or model are retained, and may be used to identify actors for other inputs. A command such as “unlock” may be executed in response to actor input. Parameters may be applied to further define where, when, how, etc. actor input is executed, such as defining a region for a gesture. The apparatus includes a processor and sensor, the processor defining actor input, identifying salient features, defining a model therefrom, and retaining a data set. A display may also be used to show actor input, a defined region, relevant information, and/or an environment. A stylus or other non-human actor may be used.

System for performing fully automatic brain tumor and tumor-aware cortex reconstructions upon receiving multi-modal MRI data (T1, T1c, T2, T2-Flair). The system outputs imaging which delineates distinctions between tumors (including tumor edema, and tumor active core), from white matter and gray matter surfaces. In cases where existing MRI model data is insufficient then the model is trained on-the-fly for tumor segmentation and classification. A tumor-aware cortex segmentation that is adaptive to the presence of the tumor is performed using labels, from which the system reconstructs and visualizes both tumor and cortical surfaces for diagnostic and surgical guidance. The technology has been validated using a publicly-available challenge dataset.

Patient, user, and/or AI information are used in a multi-objective optimization to select one of a plurality of available AIs for a task. On a patient or user-specific basis, an optimal AI is selected and applied for medical imaging or other healthcare actions. The selection may be before application, avoiding costs of applying multiple AIs to obtain the best results. The optimization may be based on statistical feedback from the user for various of the available AIs, providing information not otherwise available. The optimization may be based on AI performance, AI inclusion and/or exclusion criteria, and/or pricing information. By using optimization based on various information related to the patient, user, and/or available AI, the application of AI for a given user and/or patient by the computer may be improved. The computer operates better to provide more focused information through AI application.

Methods and systems for improving segmentation of a digital model of a patient's dentition into component teeth.

A method for dental treatment may include receiving a plurality of images of a patient's dentition, identifying, from the plurality of images, individual teeth of the patient's dentition, detecting, from the plurality of images, one or more attachments on the patient's dentition, assigning, based on each of the plurality of images, each of the one or more attachments to one of the individual teeth in each image, and combining the assignments of each of the plurality of images for attachment detection results.

Embodiments of the present disclosure provide a method and apparatus for generating a background-free image, a device, and a medium. The method may include: determining an image proportion of a target subject in an original image; clipping, in response to the image proportion being less than a preset threshold, a subject region image corresponding to the target subject from the original image; determining a corresponding subject region mask based on the subject region image; and synthesizing the subject region mask and the subject region image to generate a background-free image corresponding to the original image.

A method is provided for measuring or estimating stress distributions on heart valve leaflets by obtaining three-dimensional images of the heart valve leaflets, segmenting the heart valve leaflets in the three-dimensional images by capturing locally varying thicknesses of the heart valve leaflets in three-dimensional image data to generate an image-derived patient-specific model of the heart valve leaflets, and applying the image-derived patient-specific model of the heart valve leaflets to a finite element analysis (FEA) algorithm to estimate stresses on the heart valve leaflets. The images of the heart valve leaflets may be obtained using real-time 3D transesophageal echocardiography (rt-3DTEE). Volumetric images of the mitral valve at mid-systole may be analyzed by user-initialized segmentation and 3D deformable modeling with continuous medial representation to obtain, a compact representation of shape. The regional leaflet stress distributions may be predicted in normal and diseased (regurgitant) mitral valves using the techniques of the invention.

Systems and methods are disclosed for generating synthetic medical images, including images presenting rare conditions or morphologies for which sufficient data may be unavailable. In one aspect, style transfer methods may be used. For example, a target medical image, a segmentation mask identifying style(s) to be transferred to area(s) of the target, and source medical image(s) including the style(s) may be received. Using the mask, the target may be divided into tile(s) corresponding to the area(s) and input to a trained machine learning system. For each tile, gradients associated with a content and style of the tile may be output by the system. Pixel(s) of at least one tile of the target may be altered based on the gradients to maintain content of the target while transferring the style(s) of the source(s) to the target. The synthetic medical image may be generated from the target based on the altering.

Provided is a vision sensor including a pixel array including a plurality of pixels disposed in a matrix form, an event detection circuit configured to detect whether an event has occurred in the plurality of pixels and generate event signals corresponding to pixels from among the plurality of pixels in which an event has occurred, a map data processor configured to generate a timestamp map based on the event signals, and an interface circuit configured to transmit vision sensor data including at least one of the event signals and the timestamp map to an external processor, wherein the timestamp map includes timestamp information indicating polarity information, address information, and an event occurrence time of a pixel included in an event signal corresponding to the pixel.