A system determines an object-design for a three-dimensional model of an object. The object-design may exhibit a design continuity. The system breaks the object-design in to spatial patterns corresponding to the discrete surfaces making up the outward surface of the object. The system then generates flattened patterns by projecting the spatial patterns into a two-dimensional plane. The system prints the flattened patterns on to designated regions of material sheets in an orientation that preserves the design continuity of the object-design. The regions may be extracted from the sheets and then joined at their edges to form a cover for object that exhibits the continuity of the object design.

An image processing device for causing a display to display, as a three-dimensional image, three-dimensional data representing a biological tissue includes: a control unit configured to form, in the three-dimensional data, a cutting region exposing a lumen of the biological tissue in the three-dimensional image, and cause the display to display, together with the three-dimensional image, a two-dimensional image representing a cross section of the biological tissue and a region corresponding to the cutting region in the cross section.

A method for generating a patch-loaded Multi-Planar Reconstruction (MPR), comprising the steps of: parsing a volumetric image into a plurality of patches for storage; and loading the stored patch corresponding to the targeted region of interest requested by a user for display in at least one of a plane of the generated load-patched MPR.

The present disclosure provides methods and systems for printing a three-dimensional part. The method comprises: receiving in computer memory a digital model of said three-dimensional object; using one or more computer processors to partition said digital model of said three-dimensional part into a plurality of partitions, wherein a given partition of said plurality of partitions comprises a plurality of slices, and wherein a given slice of said plurality of slices comprises a plurality of segments; receiving, from a user, one or more parameters that specify a printing configuration for at least one segment of said plurality of segments; and generating printing instructions based at least in part on said one or more parameters, which printing instructions are usable by a print head to print said three-dimensional object.

Systems and methods are provided for multi-schema analysis of patient specific anatomical features from medical images. The system may receive medical images of a patient and metadata associated with the medical images indicative of a selected pathology, and automatically classify the medical images using a segmentation algorithm. The system may use an anatomical feature identification algorithm to identify one or more patient specific anatomical features within the medical images by exploring an anatomical knowledge dataset. A 3D surface mesh model may be generated representing the one or more classified patient specific anatomical features, such that information may be extracted from the 3D surface mesh model based on the selected pathology. Physiological information associated with the selected pathology for the 3D surface mesh model may be generated based on the extracted information.

According to at least some example embodiments, a computer-readable medium stores computer-executable program instructions that, when executed by a processor, cause the processor to perform operations including, obtaining plane information of a plane by using first distances from a terminal to a plurality of points on the plane; obtaining a normal vector of the plane by using direction information of the terminal measured by a direction sensor and the plane information; determining, based on the normal vector, a parameter of an object to be displayed on the plane; and displaying, on a display of the terminal, the object according to the determined parameter.

The invention relates to a method for constructing a dental component (I) using a graphic representation of a dental condition. A 3D volume model of the dental condition is thus already correlated with a 3D surface model of the dental condition, wherein the graphic representation of the 3D volume model is restricted to relevant regions by delimitation using the 3D surface model or using a section thereof, wherein the remaining regions of the 3D volume model are hidden.

A method for visualizing a tubular object from a set of volumetric data may include the steps of: determining a viewing direction for the tubular object; selecting a constraint subset of the tubular object within the volumetric data; defining a cut-surface through the volumetric data and including the constraint subset of the tubular object within the volumetric data; and rendering an image based upon the determined viewing direction and the volumetric data of the tubular object along the intersection of the volumetric data and the defined cut-surface. Additionally or alternatively, the method may identify a plurality of bifurcations in the tubular object; assign a weighting factor to each identified bifurcation; determine a bifurcation normal vector associated with each bifurcation; determine a weighted average of the bifurcation normal vectors; and render an image of the volumetric data from a perspective parallel to the weighted average of the bifurcation normal vectors.

A three-dimensional object data generation apparatus includes a setting unit that sets a basic voxel group defined using at least one voxel in accordance with a shape of a surface of a three-dimensional object on a basis of three-dimensional object data indicating the surface of the three-dimensional object using at least either plural flat surfaces or a curved surface and a generation unit that generates three-dimensional object data in which voxels are set inside the surface on a basis of the basic voxel group.

A method of generating a 3D microstructure using a neural network includes configuring an initial 3D microstructure; obtaining a plurality of cross-sectional images by disassembling the initial 3D microstructure in at least one direction of the initial 3D microstructure; obtaining first output feature maps with respect to at least one layer that constitutes the neural network by inputting each of the cross-sectional images to the neural network; obtaining second output feature maps with respect to at least one layer by inputting a 2D original image to the neural network; generating a 3D gradient by applying a loss value to a back-propagation algorithm after calculating the loss value by comparing the first output feature maps with the second output feature maps; and generating a final 3D microstructure based on the 2D original image by reflecting the 3D gradient to the initial 3D microstructure.