A controller for preparing an image for segmenting includes a memory that stores instructions, and a processor that executes the instructions. When executed by the processor, the instructions cause the controller to perform a process that includes displaying a first modeled tissue structure of a first type, and displaying an image of a first tissue structure of the first type separate from the first modeled tissue structure. The process also includes identifying, on the first modeled tissue structure, landmarks on the first modeled tissue structure for identification on the image of the first tissue structure, and sequentially accentuating each landmark on the first modeled tissue structure. The processor identifies locations on the image of the first tissue structure for each landmark on the first modeled tissue structure. The landmarks on the first modeled tissue structure are mapped to the locations identified on the image of the first tissue structure.

A method of identifying a flange specification based on an augmented reality interface includes steps of: providing a first interface, through which an operator picks real-time images of a flange at different viewing angles, and creating a virtual flange surface according to the real-time images; providing a second interface, through which the operator picks three circumferential points corresponding to the flange, and creating a virtual flange model having a virtual outer diameter; providing a third interface, through which the operator adjusts a virtual pitch circle diameter of the virtual flange model; providing a fourth interface, through which the operator adjusts a virtual thickness of the virtual flange model; providing a fifth interface, through which the operator inputs a count of bolts; and searching a database according to the virtual outer diameter, the virtual pitch circle diameter and the virtual thickness of the virtual flange model to obtain searched results.

The invention concerns a method implemented by computer means for visualizing at least a zone of an object in at least one interface, said method comprising the following steps: obtaining at least one image of said zone, said image comprising at least one channel, said image being a 2-dimensional or 3-dimensional image comprising pixels or voxels, a value being associated to each channel of each pixel or voxel of said image, a representation of said image being displayed in the interface, obtaining at least one annotation from a user, said annotation defining a group of selected pixels or voxels of said image, calculating a transfer function based on said selected pixels or voxels and applying said transfer function to the values of each channel of the image, updating said representation of the image in the interface, in which the colour and the transparency of the pixels or voxels of said representation are dependent on the transfer function.

In implementations of segmenting objects in vector graphics images, an object segmentation system can obtain points that identify an object in a vector graphics image, and determine a region of interest in the image that includes the object based on the points that identify the object. The object segmentation system can generate a heat map from the points that identify the object in the image, and a rasterized region from rasterizing the region of interest. The object segmentation system can generate a mask from the rasterized region and the heat map, the mask identifying pixels of the object in the rasterized region, and determine, from the mask, paths of the vector graphics corresponding to the object.

Disclosed herein include systems and methods for biological object tracking and lineage construction. Also disclosed herein include cloud-based systems and methods for allocating computational resources for deep learning-enabled image analysis of biological objects. Also disclosed herein include systems and methods for annotating and curating biological object tracking-specific training datasets.

A system, apparatus and method are provided for facilitating inspection of a target object. In the context of a method, an image is received that includes the target object. The method applies a first mask to at least a portion of the image in order to mask one or more foreground objects that at least partially block view of the target object. The method further includes applying a second mask to at least a portion of the image in order to mask a background of the target object. The method additionally includes analyzing the image of the target object following application of the first and second masks in order to identify one or more regions of the target object that merit further inspection. A corresponding system and apparatus are also provided.

Provided are a medical image processing device, a medical image processing method, a program, and a medical image display system that reduce the amount of movement of a line of sight in a case in which a medical image is observed. A first medical image of a subject is displayed. A second medical image including an anatomical feature structure closest to a designated position in the first medical image is acquired. The first medical image and the second medical image are registered. A region including the identified anatomical feature structure is cut out from the second medical image based on a registration result to generate a second partial region image. The second partial region image is displayed to be superimposed on the first medical image.

A method comprising: receiving, from a user device at a first location, first image information of a surrounding environment of the user device; determining further image information of the surrounding environment of the user device at the first location, wherein the further image information is required for generating a spatial anchor in the model, wherein the spatial anchor point links the first real-world location of the user device to a corresponding location in the model; providing guidance information to the user device for capturing the further image information; receiving the further image information from the user device; and generating a spatial anchor point in the model based on the first image information and the further image information; wherein following generation of the spatial anchor point, the spatial anchor point is discoverable to one or more users of the model to provide information of the first location.

Computer vision systems and methods for generating building models using three-dimensional sensing and augmented reality (AR) techniques are provided. Image frames including images of a structure to be modeled are captured by a camera of a mobile device such as a smart phone, as well as three-dimensional data corresponding to the image frames. An object of interest, such as a structural feature of the building, is detected using both the image frames and the three-dimensional data. An AR icon is determined based upon the type of object detected, and is displayed on the mobile device superimposed on the image frames. The user can manipulate the AR icon to better fit or match the object of interest in the image frames, and can capture the object of interest using a capture icon displayed on the display of the mobile device.

A method for pre-operative orthopedic planning includes obtaining only a high-resolution knee-joint scan of a patient, determining hip rotation center and ankle rotation center from anthropometric data based on personal data of the patient, and determining a mechanical axis of the knee joint based on the anthropometric data. The method also includes preparing at least a two-dimensional image model of the knee joint using the knee-joint scan and the determined mechanical axis, and preparing a pre-operative surgical plan based on the image of the knee joint.