In part, the disclosure relates to methods, and systems suitable for evaluating image data from a patient on a real time or substantially real time basis using machine learning (ML) methods and systems. Systems and methods for improving diagnostic tools for end users such as cardiologists and imaging specialists using machine learning techniques applied to specific problems associated with intravascular images that have polar representations. Further, given the use of rotating probes to obtain image data for OCT, IVUS, and other imaging data, dealing with the two coordinate systems associated therewith creates challenges. The present disclosure addresses these and numerous other challenges relating to solving the problem of quickly imaging and diagnosis a patient such that stenting and other procedures may be applied during a single session in the cath lab.

A method of displaying a nearby point of interest (POI) using augmented reality and an electronic device thereof. An operation method of an electronic device includes: acquiring a preview video; acquiring location information of the electronic device; acquiring information on at least one POI located in a vicinity; selecting a focus POI among at least one POI; determining first information to be provided to a user for general POIs; determining second information including more information than the first information to be provided to the user for the focus POI; determining a location on a screen on which the preview video in which the information on the at least one POI is to be displayed; and displaying the first information or the second information on the location on the screen on which the determined preview video of all or some of the at least one POI is provided.

Described herein are platforms, systems, media, and methods for measuring a space by launching an active augmented reality (AR) session on a device comprising a camera and at least one processor; calibrating the AR session by establishing a fixed coordinate system, receiving a position and orientation of one or more horizontal or vertical planes in the space in reference to the fixed coordinate system, and receiving a position and orientation of the camera in reference to the fixed coordinate system; constructing a backing model; providing an interface allowing a user to capture at least one photo of the space during the active AR session; extracting camera data from the AR session for the at least one photo; extracting the backing model from the AR session; and storing the camera data and the backing model in association with the at least one photo.

Various examples are provided related to identification of anthropometric information for fitting of compression garments. In one example, a method of generating compression garment fit information includes acquiring images including a selected body part or body area in need of compression therapy; processing the acquired images along with a library of compression garment fit information; generating a 3D reconstruction of the selected body part/area; deriving anthropometric information for the selected body part/area from the 3D reconstruction; providing a compression value corresponding to a prescribed or intended amount of compression therapy applied to the selected body part/area; and generating compression garment fit information for the selected body part/area. In another example, a library of compression garment fit information includes anthropometric information generated for individuals in need of compression therapy; information associated with a health condition for the individuals; and compression garment fit instructions provided by a compression garment manufacturer.

A method of finding a set of corresponding points in images to be registered. According to the method, an input unit of the system receives a first user input, indicative of a reference point in an intraoperative image. A processing unit sets a reference area surrounding the reference point and converts the image data points in the reference area to a intraoperative point cloud. The input unit receives a second user input, indicative of a candidate point in a preoperative image. The processing unit sets a search area surrounding the reference point and converts the image data points in the reference area to a preoperative point cloud. By comparing geometric feature descriptors of the image data points, the processing unit finds a target point in the preoperative image corresponding to the reference point and defines the points as set of corresponding points.

Disclosed herein is a medical system (100, 300) comprising a display (112) and a user interface (114). The execution of machine executable instructions (120) causes a processor (104) to: receive (200) three-dimensional medical image data (122) of an anatomical structure (128, 322); receive (202) a three-dimensional segmentation (124) with one or more reference locations (800); display (204) at least one two-dimensional slice (126) of the three-dimensional medical image data; render (206) a cross section (134) of the three-dimensional segmentation, provide (208) a control element (130) of the user interface that is configured for receiving a one-dimensional position of the at least one reference location along a predetermined one-dimensional path (806); receive (210) the one-dimensional position (137) from the control element; adjust (212) the three-dimensional segmentation (124) using the one-dimensional position; and update (214) the rendering of the cross section of the three-dimensional segmentation.

A method of determining an installation site of a robot manipulator at a workstation, the method including: recording a respective image of the robot manipulator and of the workstation of the robot manipulator, and of a workpiece to be machined at the workstation via a camera unit, wherein the respective image contains spatial information; transmitting the respective image to a computing unit; and determining the installation site of the robot manipulator by applying a non-linear optimization of a predefined cost function and/or of a neural network via the computing unit based on a predefined task for machining the workpiece and based on the spatial information determined by the computing unit from the respective image.

Embodiments include systems and methods for determining a 6D pose estimate associated with an image of a physical 3D object captured in a video stream. An initial 6D pose estimate is inferred and then further iteratively refined. The video stream may be frozen to allow the user to tap or touch a display to indicate a location of the user-input keypoints. The resulting 6D pose estimate is used to assist in replacing or superimposing the physical 3D object with digital or virtual content in an augmented reality (AR) frame.

A subject support (14) is configured to dock with a medical imaging device (50) with a fixed spatial relationship between the docked subject support and the medical imaging device. A patient positioning device includes a range camera (10) that acquires a two-dimensional (2D) range image of a human imaging subject (12) disposed on a subject support (14). The range image has pixel values corresponding to distances from the range camera. An electronic processor (16) is programmed to perform a positioning method to determine a reference point on or in the human subject in a frame of reference (F.sub.S) of the subject support from the 2D range image. This reference point is translated to a frame of reference (F.sub.D) of the imaging device based on a priori known spatial relationship of the medical imaging device and the docked subject support. Using 3D models, the loading process may be simulated.

A method of visualizing a treatment of teeth includes maintaining a final position model trained to output movement of teeth from initial to final positions, receiving from a user device a first representation comprising teeth of a user in an initial position where the first representation is based on a 2D image transmitted from the user device, determining tooth movements of the teeth from the initial position to a final position using to the final position model, receiving by the user device a graphical visualization of a treatment plan for moving the teeth of the user where the graphical visualization is generated based on the final position and the treatment plan is suitable for correcting the user's malocclusion, and displaying by the user device the graphical visualization where the graphical visualization comprises a three-dimensional (3D) representation corresponding to the teeth of the user.