13 results about "Marching cubes" patented technology

The invention discloses a three-dimensional visualization head and neck tumor preoperative imaging system based on deep learning. The method comprises the following steps: image analysis and preprocessing; carrying out image denoising and spatial-temporal feature extraction based on a deep learning convolutional neural network architecture, and introducing a u-Net model to realize image segmentation and semantic annotation; performing three-dimensional reconstruction and surface rendering by adopting a Marking Cubes algorithm, and realizing visual interaction image presentation and personalized operation by combining volume rendering and surface rendering; and data is output through multiple ports, and data viewing and interactive operation of a web terminal and a mobile terminal are supported. The method has the advantages that according to the technical scheme, through fusion of a three-dimensional attention mechanism and multi-phase data, the tumor segmentation Dice coefficient reaches 0.92 + / -0.03, and the method is superior to a traditional threshold segmentation method.

The invention relates to the technical field of medical image processing and three-dimensional reconstruction, and particularly discloses a reconstruction and analogue simulation method based on a cardiac image. The method comprises the following steps: firstly, preprocessing an input CT or MRI medical image, and automatically segmenting a heart multi-tissue structure by using an nnUNetv2 model, including a plurality of anatomical regions such as atrium, ventricle, myocardial tissue and blood pool; and then performing topology repair, smoothing processing and label resampling on the segmentation result to obtain a three-dimensional label body with a continuous structure and a complete boundary. And based on the optimized tag body, constructing a heart three-dimensional surface model by adopting a Marching Cubes algorithm, and generating a multi-material volume grid suitable for finite element analysis. According to an electrode position and a radio frequency parameter set by a user, a radio frequency ablation simulation model based on a Pennes biological heat conduction equation and an Arrhenius model is established, a temperature field and a tissue thermal damage range are calculated, and a simulation result is displayed in a three-dimensional mode in an overlapping mode. According to the method, the integrated process from medical image automatic segmentation to heart three-dimensional reconstruction and thermal simulation is realized, the segmentation precision, the modeling efficiency and the simulation reliability are improved, and the method can be applied to application scenes such as surgical planning, preoperative evaluation and medical teaching.

The application discloses a brain glioma segmentation and three-dimensional visualization model training method and system based on multi-modal fusion, and comprises the following steps: inputting multi-modal medical image data into a Laplacian pyramid multi-modal fusion learning model to generate a fusion graph, then inputting the fusion graph into a U-net segmentation model comprising an encoder and a decoder to obtain a segmentation mask under the multi-modal fusion graph; and finally, based on the multi-modal fusion graph and the segmentation mask, adopting a Marching cubes (MC) algorithm to perform three-dimensional reconstruction visualization of brain regions and brain tumor region labeling. The brain glioma segmentation and three-dimensional visualization model training method and system based on multi-modal fusion provided by the application optimizes the disadvantages of insufficient information and incomplete modeling under single modal, fully utilizes the advantages of the completeness of multi-modal data, performs multi-modal fusion, segmentation and three-dimensional visualization, can more comprehensively assist surgeons in treatment, and improves the accuracy in the operation process.

The application discloses a kind of tropical cyclone temperature body structure calculation method and system, wherein the method includes: based on satellite microwave detection data and tropical cyclone position information, the three-dimensional temperature data of tropical cyclone area is smoothed, and temperature grid with tropical cyclone center as origin is formed.Three-dimensional temperature anomaly value is calculated according to the smoothed temperature grid.Based on three-dimensional temperature anomaly value, three-dimensional region growth method is used to extract tropical cyclone warm core main area.Based on the extracted tropical cyclone warm core main area, the isosurface is calculated using Marching Cubes algorithm.According to the isosurface, the light model is rendered to draw the three-dimensional visualization structure of tropical cyclone warm core.Based on the three-dimensional temperature anomaly value of tropical cyclone warm core main area, the tropical cyclone warm core intensity index is calculated.Thus, the application effectively solves the problem of insufficient overall understanding of tropical cyclone temperature structure in the prior art, and provides the ability of systematic quantitative analysis.

The invention relates to the technical field of tunnel face surrounding rock grading in railway construction, and provides a drilling and blasting method tunnel face surrounding rock three-dimensional refined grading method based on a seismic wave reflection method, which comprises the following steps: collecting three-dimensional inversion dot matrix data of an area in front of a tunnel face; the data coordinate system is converted into a tunnel design coordinate system, the surrounding rock basic quality index BQ value of each point is calculated according to the wave velocity, and a space BQ value dot matrix database is generated; performing spatial region division on the BQ value dot matrix by adopting a three-dimensional region self-growth method to obtain a connected region with similar BQ value characteristics; after the representative value of the region boundary is determined, using a Marking Cubes algorithm to extract contour surfaces to construct a surrounding rock three-dimensional grid model; and finally, mapping the model vertex BQ value with a preset grading standard, and outputting a three-dimensional refined grading model with a surrounding rock grading label. Automatic processing and three-dimensional visualization of surrounding rock classification are achieved, and classification accuracy and construction decision reliability are improved.

The application discloses a multi-radar power envelope fusion method, comprising: single-radar envelope modeling, constructing envelope fusion foundation; determining non-coherent field fusion, constructing multi-radar envelope model; constructing gradient diffusion model, realizing envelope fusion smooth transition; displaying Euler method iteration convergence, realizing multi-radar envelope complete fusion; adopting adaptive octree structure to dynamically divide three-dimensional space. Advantageous effects: the multi-radar power envelope fusion method firstly calculates single-station detection probability of each radar unit at a grid point in a three-dimensional space voxel according to a radar equation, a propagation loss model and target scattering characteristics; then adopts a probability union or a more complex dependent relationship model to generate a full-space joint detection probability field; finally, an isosurface extraction (such as an improved Marching Cubes algorithm) and a probability gradient driven color / transparency mapping are adopted to generate a visual continuous, mathematically accurate fusion envelope surface.

This invention discloses a method and system for constructing a mesh model based on the spatial parameters of a 3D Gaussian surface model, belonging to the field of 3D reconstruction technology. It includes at least the following steps: establishing an initial 3D Gaussian elliptical surface model based on the sparse point cloud of the reconstructed object; constructing a local implicit function field for each Gaussian elliptical surface model; for a single Gaussian elliptical surface model, the implicit function value of a vertex is the signed distance from the voxel vertex to the corresponding Gaussian elliptical surface; performing global weighted fusion based on the local implicit function field of the current Gaussian elliptical surface model; and extracting 0-isosurfaces using the Marching Cubes algorithm based on the vertex function values ​​of the global voxel field, thereby constructing the mesh model of the reconstructed object. This invention establishes a voxel field consistent with the distribution of the 3D Gaussian elliptical surface through the 3D spatial parameters of the 3D Gaussian elliptical surface model, enabling accurate and unobstructed surface representation and achieving high-precision 3D reconstruction of the mesh model.

Disclosed herein are an apparatus and method for compressing 3D volume data. The apparatus for compressing three-dimensional (3D) volume data includes one or more processor, and execution memory configured to store at least one program that is executed by the one or more processors, wherein the at least one program is configured to receive 3D volume data for data compression, select necessary voxels that contribute to formation of a geometric surface from the 3D volume data using a marching cubes algorithm, represent location information of the selected necessary voxels by a tree structure, and binarize the location information of the necessary voxels by serializing the tree structure, and quantize truncated signed distance values to surfaces adjacent to the necessary voxels constituting a volume from the 3D volume data using the binarized location information of the necessary voxels.

The invention relates to the technical field of tunnel advanced geological forecast, and discloses a railway tunnel construction tunnel face front surrounding rock water abundance three-dimensional model construction method comprising the following steps: collecting a plurality of transient electromagnetic survey lines in a tunnel face direction, and uniformly mapping two-dimensional inversion results to a right-handed coordinate system according to survey line angles, mileages and survey point distances; and constructing a spatial water-rich database. After geometric parameters of the tunnel face are obtained, a three-dimensional cube grid is established, and apparent resistivity values are given to nodes. A kd tree is constructed based on node distribution, a normal vector and curvature are calculated, and a mark array is initialized. And completing node clustering by adopting a three-dimensional region self-growth algorithm in combination with attribute difference, a region threshold and curvature constraint to form a water-rich region. And a binary volume data field is constructed for each clustering region, contour surfaces are extracted by using Marching Cubes, and a surrounding rock water yield three-dimensional model is generated. According to the method, high-precision three-dimensional modeling of the water-rich area in front of the tunnel face is effectively constructed, and the accuracy of advanced prediction of the tunnel is remarkably improved.

The invention belongs to the technical field of three-dimensional modeling, and particularly relates to an unsupervised non-watertight point cloud high-fidelity reconstruction method. Aiming at the problems of inconsistent gradient directions, local detail loss, artifact fragmentation and the like in open surfaces, non-watertight point clouds and complex geometric scenes in the existing method, the invention provides a normalization and density adaptive resampling mechanism; symmetric point pairs are generated through local normal estimation, and prior constraints with consistent UDF values and opposite gradients are applied; designing a multi-scale Gabor code with spatial locality and direction selectivity, and combining the combined feature input of a symmetric invariant base and a symmetric variant base; gradually converging from global topology to local details by adopting progressive multi-scale sampling and composite unsupervised loss function optimization; and a UDF curved surface extraction method based on gradient discrimination and improved Marking Cubes is provided.

A kind of orthopedic surgery navigation registration method based on UWB marker point, UWB module is used as calibration point around the patient's affected part to calibrate, DICOM data is obtained by scanning medical imaging equipment, convolutional neural network is combined with integrated learning algorithm Xgboost to pre-process medical imaging data, finally, three-dimensional reconstruction is carried out by surface rendering Marching Cubes method, the spatial coordinate system of calibration point is obtained by UWB positioning algorithm, according to the number of registration points in actual spatial coordinate system and virtual spatial coordinate system, the optimal transformation matrix is found, so that the transformed coordinates are closest to virtual coordinate system coordinates.Translation transformation matrix and rotation transformation matrix are solved by SVD matrix decomposition method, scale transformation parameter is solved by quaternion algorithm combined with homogeneous coordinate theory, when marker point moves, repeat the steps to complete real-time registration and registration based on UWB marker point.Avoid the movement of intraoperative non-active factors and need to recalibrate.

The application discloses a kind of turning processing workpiece surface three-dimensional topography prediction method, comprising the following steps: step one: the Marching Cubes contour surface of target surface is constructed: according to the range of target surface, constructs a cubic grid;The value of implicit function is operated on each grid point in cubic grid;According to the sign of implicit function value, based on Marching Cubes algorithm, generate triangular patches and the corresponding vertex value of triangle;Step two: the target surface is uniformly randomly sampled: according to the established triangular contour surface grid, uniformly randomly in each triangular grid inside sampling point is obtained, to realize the uniform random sampling of target surface;Step three: three-dimensional topography prediction is carried out to target surface: based on the tool position data of complex free surface path planning, tool path is modeled, and residual height on each random sampling point is calculated, realizes three-dimensional topography prediction to target surface.

The invention discloses a preoperative focus recognition system based on multi-modal image fusion, and belongs to the technical field of medical image processing and computer-aided surgery. The system comprises an OCT image modeling module which is used for reconstructing an OCT three-dimensional tissue volume model through an FFT (Fast Fourier Transform) method, structure tensor enhancement and a Marching Cubes algorithm; the microscope image registration module adopts SIFT (scale invariant feature transform) feature extraction, K-D tree matching, RANSAC (random sample consensus) and ICP (inductively coupled plasma) optimization and Gamma correction to realize sub-pixel-level space and luminosity registration of a microscope image and an OCT (optical coherence tomography) model; the multi-modal fusion modeling module is used for adaptively fusing OCT structural features and microscope texture features by using a dual-channel deep neural network and a cross-modal attention mechanism; the focus segmentation and positioning module is used for realizing accurate segmentation and three-dimensional positioning of the focus by adopting an improved U-Net network and a joint loss function; and the preoperative planning module automatically generates an operation point and an orbit injection scheme of a surgical instrument through ellipsoid fitting and a genetic algorithm. The lesion recognition precision and the operation planning efficiency are remarkably improved.