CN111862171A - CBCT and laser scanning point cloud data tooth registration method based on multi-view fusion - Google Patents
CBCT and laser scanning point cloud data tooth registration method based on multi-view fusion Download PDFInfo
- Publication number
- CN111862171A CN111862171A CN202010772778.XA CN202010772778A CN111862171A CN 111862171 A CN111862171 A CN 111862171A CN 202010772778 A CN202010772778 A CN 202010772778A CN 111862171 A CN111862171 A CN 111862171A
- Authority
- CN
- China
- Prior art keywords
- point cloud
- cloud data
- model
- point
- dental crown
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000007408 cone-beam computed tomography Methods 0.000 title claims abstract description 21
- 230000004927 fusion Effects 0.000 title claims abstract description 21
- 238000009877 rendering Methods 0.000 claims abstract description 41
- 230000006870 function Effects 0.000 claims abstract description 22
- 238000005070 sampling Methods 0.000 claims abstract description 18
- 238000000605 extraction Methods 0.000 claims abstract description 3
- 239000013598 vector Substances 0.000 claims description 30
- 230000009466 transformation Effects 0.000 claims description 12
- 238000001914 filtration Methods 0.000 claims description 10
- 239000011159 matrix material Substances 0.000 claims description 9
- 238000013527 convolutional neural network Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 230000008030 elimination Effects 0.000 claims description 5
- 238000003379 elimination reaction Methods 0.000 claims description 5
- 238000013507 mapping Methods 0.000 claims description 5
- 238000009826 distribution Methods 0.000 claims description 4
- 238000000638 solvent extraction Methods 0.000 claims description 4
- 238000013519 translation Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 239000002131 composite material Substances 0.000 claims description 3
- 238000000354 decomposition reaction Methods 0.000 claims description 3
- 238000007499 fusion processing Methods 0.000 claims description 3
- 210000004746 tooth root Anatomy 0.000 claims description 3
- 230000003044 adaptive effect Effects 0.000 claims description 2
- 230000011218 segmentation Effects 0.000 claims description 2
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 125000004122 cyclic group Chemical group 0.000 abstract description 2
- 238000005259 measurement Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 3
- 238000002513 implantation Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 239000004053 dental implant Substances 0.000 description 1
- 210000004513 dentition Anatomy 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012805 post-processing Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 210000000332 tooth crown Anatomy 0.000 description 1
- 230000036346 tooth eruption Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
- G06T2207/10012—Stereo images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30036—Dental; Teeth
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a tooth registration method based on multi-view fusion for CBCT and laser scanning point cloud data. The method comprises the steps of obtaining complete model point cloud data and dental crown model point cloud data, and then carrying out down-sampling to reduce the point cloud data volume; respectively performing view rendering to obtain a complete model rendering output and a dental crown model rendering output; then, performing feature extraction on the two rendering outputs to obtain complete model features and crown model features; registering the two features to obtain a fused image; finally, constructing a loss function and correcting the fused image; the invention provides an end-to-end framework for learning a local multi-view descriptor of a 3D point cloud; fusing functions between views together; the invention establishes the cyclic consistency across multiple scans, and is beneficial to solving the problem of low registration precision caused by low overlapping rate of adjacent point clouds, high symmetry and repeated scene parts.
Description
Technical Field
The invention relates to the technical field of medical image processing, in particular to a CBCT and laser scanning point cloud data tooth registration method based on multi-view fusion.
Background
In the field of oral medicine, orthodontic treatment requires a complete three-dimensional dental model for diagnosis, making an orthodontic treatment plan, monitoring tooth movement and other processes, and meanwhile, the complete and accurate three-dimensional dental model is also a necessary premise of a dental implant or orthognathic surgical plan, and a doctor can understand the three-dimensional space of a focus part of a patient more deeply through the three-dimensional model. However, none of the existing imaging techniques is capable of simultaneously acquiring and integrating all of the anatomical tissues involved in clinical implantation, orthodontic practice. Currently, there are two mainstream techniques for obtaining three-dimensional dental models: obtaining a three-dimensional dental model based on laser scanning and reconstructing the three-dimensional dental model using Cone Beam Computed Tomography (CBCT) images, wherein only crown three-dimensional information and mucosal surface information are provided based on laser scanning, only the surface information is saved, and no further information can be provided for clinical treatment; the CBCT image reconstruction can obtain a complete three-dimensional tooth model, but the resolution of the CBCT image is low, so that the reconstructed model is inaccurate in information of a coronal region, and therefore, the CBCT image reconstruction cannot be directly used for designing personalized orthodontic appliances. Therefore, the fusion registration of the CBCT and the laser scanning point cloud data tooth and the construction of a complete three-dimensional tooth model are necessary so as to assist tooth implantation and orthodontic treatment.
Point clouds have become an increasingly common representation of the 3D world over the last few decades. The point cloud collected by the laser scanner or the three-channel color image camera can be used for landslide monitoring, solar potential analysis, three-dimensional model reconstruction, cultural heritage protection, forest management, robot self-localization and automatic driving automobile high-definition map manufacturing. The existing point cloud registration methods are mainly classified into the following types: a deep learning based registration method, an improved Iterative Closest Point (ICP) based registration method, a semi-supervised based registration method, a hyper-voxel based registration method, an end-to-end based registration method. Among many registration methods, the ICP algorithm introduced in the early 90 s of the 20 th century is the most famous algorithm for efficiently registering two-dimensional or three-dimensional point sets under the euclidean (rigid) transformation, and the concept of the ICP algorithm is simple and intuitive. While ICP reduces some objective function measurement alignment, ICP often falls into sub-optimal local extrema due to the non-convexity of the problem and the need for good initialization, otherwise it easily falls into local minima. To address the local optimization of ICP and other difficulties, a number of improved algorithms based on ICP algorithms have been derived. The algorithms are optimized from the aspects of removing mismatching points, constructing an error measurement function, solving the error function and the like. The registration method based on semi-supervision adopts feature measurement projection errors, and compared with geometric projection errors, the registration method has the advantages of robustness to noise, outliers and density differences. In addition, the projection error of the characteristic quantity is minimized, and the corresponding relation does not need to be searched, so that the optimization speed is high; semi-supervised registration methods require limited or no registration tag data. The method has higher accuracy and robustness, can process obvious noise and density difference, and can solve the problems of homologous and cross-source point cloud registration. A color point cloud data registration method based on hyper-voxels assists the initial geometric alignment of point clouds by adding color information of the point clouds, and a feature representation method of mixed color moments is designed, wherein the color moments reflect the color distribution of local areas at a high level. And performing similarity measurement through the joint features, and dynamically adjusting the weight between the color and the spatial information. By filtering out the fuzzy characteristic relation, the transformation estimation can be more accurately carried out robust registration. End-to-end matching based on two point clouds, while showing good performance for many tasks, still has some drawbacks: (1) low overlap of adjacent point clouds may result in inaccurate or false matches; (2) the registration of the point cloud must rely on very local information, which can lead to false point cloud matching if the 3D scene structure is sparse or repetitive; (3) to merge all pairs of point cloud matches into one global representation, separate post-processing is required, which makes the process very cumbersome.
Disclosure of Invention
In order to meet the complete tooth or dentition model of clinical requirements, the invention provides a CBCT and laser scanning point cloud data tooth registration method based on multi-view fusion, so as to overcome the defects of the existing point cloud registration technology.
The invention discloses a CBCT and laser scanning point cloud data tooth registration method based on multi-view fusion, which comprises the following steps:
1) acquiring point cloud data of different sources:
extracting a tooth complete model from CBCT data according to a region growing method, converting the tooth complete model into complete model point cloud data, extracting a dental crown three-dimensional model by adopting a laser scanner, and converting the dental crown three-dimensional model into dental crown model point cloud data;
2) denoising the complete model point cloud data and the dental crown model point cloud data respectively by using a statistical outlier elimination filter to obtain denoised complete model point cloud data and dental crown model point cloud data;
3) respectively carrying out down-sampling on the denoised complete model point cloud data and the dental crown model point cloud data to reduce the point cloud data volume;
4) and performing view rendering:
manually picking up one or more characteristic points q of each tooth by using the full model point cloud data and the crown model point cloud data after down samplingjEach feature point is regarded as a spherical surface, j is 1jAverage distance to its local neighbors; the view rendering is to generate a 3D local geometric figure by performing perspective projection with a viewpoint and select a fixed viewpoint ck to perform downsampling on the characteristic points q of the complete model point cloud data and the crown model point cloud datajPerforming perspective projection to obtain characteristic point qjGenerating a corresponding probability map DjProbability map DjComprises a plurality of output pixels, each output pixel is described by a characteristic point qjProbability of coverage, all output pixels constituting a rendering output T, the output T of the ith pixel in the rendering output TiComprises the following steps:
where I1.. multidot.i, I is the number of output pixels, CjIs a point qjRendering attribute of, CbAs a default background value, zjIs qjDepth of (a), w (-) and wbIs a weighting function and
Djis a characteristic point qjThe probability map of the output is then calculated,
representing the probability distribution of the ith pixel so as to respectively obtain the rendering output of the complete model and the rendering output of the dental crown model;
5) performing feature extraction on the rendering output:
learning a local image descriptor by adopting a Convolutional Neural Network (CNN), and extracting corresponding characteristics of each tooth from the rendering output of the complete model and the rendering output of the dental crown model respectively to obtain the characteristics of the complete model and the dental crown model;
6) fusing multiple views:
the connection relation between the complete model characteristic and the same part in the dental crown model characteristic is called a group of characteristic maps, and the characteristic maps are used for mapping
As input, performing feature fusion, and realizing registration of the complete model feature and the dental crown model feature to obtain a fusion image;
7) constructing a loss function:
wherein,
for the target corresponding point, N is the characteristic point of the complete model point cloud data and the dental crown model point cloud data extracted after the step 4)Number, yiFor estimating the corresponding point of the target, R is a rotation transformation matrix, T is a translation transformation matrix, and the Loss function obtained by directly calculating the Euclidean distance is Loss1Considering that the registration task is also constrained by the global geometric transformation, another Loss including the global geometric constraint is introduced2；
The composite loss function is:
Loss＝aLoss1+(1-a)Loss2
wherein a is Loss1And Loss2The weight of the intermediate weight is 0.1-0.2;
8) and correcting the fused image by constructing a loss function.
In step 1), the tooth complete model extracted from the CBCT data includes a crown and a root part, and is a complete tooth model, but the resolution is low. The laser scanning provides a crown three-dimensional model with high resolution, but only provides crown three-dimensional information and mucosal surface information.
In the step 2), denoising the point cloud data by using a statistical outlier elimination filter, comprising the following steps:
a) calculating the average distance d from each data point in the point cloud data to the nearest k neighborhood points by adopting a k nearest neighbor algorithm, wherein k is the number of the selected neighborhood points and is set to be 1-10;
b) calculating an expected value dm and a standard deviation s of the average distance d;
c) calculating a formula for calculating the distance threshold dt according to the expected value dm and the standard deviation s,
dt＝dm+λ×s
wherein lambda is a standard deviation parameter, and the value of lambda is 0.1-0.3;
d) and comparing the average distance d of each characteristic point with a distance threshold dt, if d > dt, filtering the point, and otherwise, keeping the point.
In the step 3), the denoised complete model point cloud data and crown model point cloud data are respectively subjected to down-sampling, and the method comprises the following steps:
(1) search space partitioning of point cloud data
Determining the sizes of the complete model point cloud data and the crown model point cloud data in space, obtaining X, Y and the minimum and maximum values of the Z coordinate axes are Min _ x, Max _ x, Min _ y, Max _ y, Min _ Z and Max _ Z respectively, and constructing the maximum space range of the tooth point cloud: [ Min _ x, Max _ x ] × [ Min _ y, Max _ y ] × [ Min _ z, Max _ z ], maximum range for tooth point cloud
Performing space segmentation to obtain a maximum bounding box L of the tooth point cloud:
wherein beta is a size factor for adjusting the maximum tooth bounding box, the value is 0.5-0.8, and k is the number of selected field points and is set to be 1-10;
(2) normal vector estimation of point cloud data
Taking the normal vector of the data point of each complete model point cloud data and the dental crown model point cloud data in the maximum bounding box L as the normal vector of the fitting tangent plane of the data point, calculating the fitting tangent plane of each data point, and finishing the plane fitting of the local area of the data point of each complete model point cloud data and the dental crown model point cloud data;
(3) curvature estimation of point cloud data
a) Respectively carrying out curvature estimation on the complete model surface point cloud data and the dental crown model surface point cloud data in the maximum bounding box L by utilizing a paraboloid fitting algorithm, wherein the main curvature of the curved surface of the complete model surface point cloud data and the main curvature of the dental crown model surface point cloud data are the same as the main curvature of the paraboloid, and the paraboloid equation is as follows:
Z＝bx2+cxy+dy2
b) let vertex x of parabolic equationlEstablishing a coordinate system of the local coordinate system and k adjacent points thereof for enabling x to be xlThe normal vector and the Z axis are merged, and the rotation is transformed into a local coordinate system of k nearest points to form a linear equation system:
AX＝Z
wherein,
X＝[b c d]T，Z＝[z1... zk]T
c) solving a formula AX (Z) by using a Singular Value Decomposition (SVD) method to obtain coefficients b, c and d, thereby obtaining a paraboloid equation;
(4) calculating the average curvature H of the complete model point cloud data and the dental crown model point cloud data by using the b, c and d coefficients:
H＝b+d
comparing the average curvature of the point cloud data of the complete model and the point cloud data of the dental crown model with a set threshold, and dividing the dental crown and the dental root in the point cloud data into: peaks, valleys and ridges, wherein the peaks correspond to cusp characteristics of the dental crown surface of the tooth body, the average curvature H >0 (i.e. the local area is convex); the valley corresponds to the groove on the tooth crown surface of the tooth body, and the average curvature H is less than 0, namely the local area is concave; the ridges correspond to various ridge lines on the tooth body, and the concave-convex shape is determined according to the curvature of adjacent points; setting a corresponding peak threshold value to be 30-50, and filtering point cloud data of which the average curvature of a peak is lower than the peak threshold value range in the point cloud data; and setting the corresponding valley threshold interval to be-50 to-30, and filtering the point cloud data of which the average curvature of the valleys is larger than the valley threshold range in the point cloud data.
In step (2), k neighboring points of the data point x are recorded as nb (x), n is a normal vector of the data point x, the normal vector n of the data point is a normal vector of a fitting tangent plane of the data point, and a least square method is used to obtain a fitting tangent plane Tp (x)i) Calculating covariance matrixes of k neighbor points Nb (x), and calculating normal vectors corresponding to the k neighbor points of the data point x according to the covariance matrixes of Nb (x); and unifying the directions of the normal vectors of the obtained fitting tangent planes, keeping the directions of the normal vectors of the adjacent planes consistent, and finishing the plane fitting of the data point local areas of each complete model point cloud data and the dental crown model point cloud data.
In step 6), a soft view pool method of sub-network self-adaptive weight estimation is used in the fusion process, the sub-network takes each feature mapping Fk as input, and regression is carried out on the corresponding weight according to the encoding-decoding process; then a sub-network executes down-sampling, up-sampling the space size and the channel depth by more than 2 times, and finally embedding the complete model characteristic and the crown model characteristic into an n-dimensional (n >3) descriptor space for characteristic fusion so as to obtain a fusion image; wherein the n-dimensional space has a fully connected layer and a normalized layer.
In step 7), the point cloud data output by the dental crown model rendering is used as a source point cloud, the point cloud data output by the complete model rendering is used as a target point cloud, the characteristic points extracted from the dental crown model rendering output and the matching complete model rendering output are adopted, the characteristic points of the point cloud data output by the actually matched complete model rendering are used as target corresponding points, and the characteristic points of the predicted point cloud data output by the complete model rendering are used as estimated target corresponding points. And matching the point cloud data output by rendering the complete tooth model with the point cloud data output by rendering the dental crown model by using the convolutional neural network so as to solve a rotation transformation matrix R and a translation transformation matrix T.
The invention has the advantages that:
the invention provides an end-to-end framework which is used for learning a local multi-view descriptor of a 3D point cloud and has the latest performance; the viewpoint can be optimized through micro-rendering in the network; fusing functions between views together; the invention establishes the cyclic consistency across multiple scans, and is beneficial to solving the problem of low registration precision caused by low overlapping rate of adjacent point clouds, high symmetry and repeated scene parts.
Drawings
FIG. 1 is a flow chart of a multi-view fusion-based CBCT and laser scanning point cloud data tooth registration method of the present invention;
FIG. 2 is a comparison graph of the effect before and after sampling of complete model point cloud data and crown model point cloud data obtained by a CBCT and laser scanning point cloud data tooth registration method based on multi-view fusion according to the present invention;
FIG. 3 is a point cloud preprocessing flow chart of the multi-view fusion-based CBCT and laser scanning point cloud data tooth registration method of the present invention.
Detailed Description
The invention will be further elucidated by means of specific embodiments in the following with reference to the drawing.
As shown in fig. 1, the tooth registration method based on CBCT and laser scanning point cloud data of multi-view fusion of the present embodiment includes the following steps:
1) acquiring point cloud data of different sources:
extracting a tooth complete model from CBCT data according to a region growing method, converting the tooth complete model into complete model point cloud data, extracting a dental crown three-dimensional model by adopting an intraoral laser scanner, and converting the dental crown three-dimensional model into dental crown model point cloud data;
2) denoising the complete model point cloud data and the dental crown model point cloud data by utilizing a statistical outlier elimination filter:
a) calculating the average distance d from each data point in the point cloud data to the nearest k neighborhood points by adopting a k nearest neighbor algorithm, wherein k is the number of the selected neighborhood points and is set as 6;
b) calculating an expected value dm and a standard deviation s of the average distance d;
c) calculating a formula for calculating the distance threshold dt according to the expected value dm and the standard deviation s,
dt＝dm+λ×s
wherein, λ is standard deviation parameter, λ is 0.2;
d) comparing the average distance d of each characteristic point with a distance threshold dt, if d is greater than dt, filtering the point, and otherwise, keeping to obtain the denoised complete model point cloud data and dental crown model point cloud data;
3) respectively carrying out down-sampling on the denoised complete model point cloud data and the dental crown model point cloud data:
(1) search space partitioning of point cloud data
Determining the sizes of the complete model point cloud data and the dental crown model point cloud data in space, obtaining X, Y and the minimum and maximum values of the Z coordinate axes are Min _ x, Max _ x, Min _ y, Max _ y, Min _ Z and Max _ Z respectively, and constructing the maximum space range L of the dental point cloud: [ Min _ x, Max _ x ] × [ Min _ y, Max _ y ] × [ Min _ z, Max _ z ], spatially partitioning the maximum spatial range yields the maximum bounding box L:
wherein, beta is a size factor for adjusting the maximum bounding box of the teeth, and k is the number of the selected field points;
(2) normal vector estimation of point cloud data
The initial sampling point set obtained by three-dimensional scanning only records the space three-dimensional coordinates of each sampling point without any connection relation, the normal vector is a local characteristic of the three-dimensional point cloud data and is essential information in a plurality of processing of the point cloud, and the solving of the normal vector is a key step for processing the point cloud data. Taking the normal vector of the data point of each complete model point cloud data and crown model point cloud data positioned in the maximum bounding box L as the normal vector of the fitting tangent plane of the data point, and taking the data point xiK neighbor points of (a) are denoted as Nb (x)i)，niIs a data point xiNormal vector, obtaining fitting tangent plane Tp (x) by least square methodi) According to Nb (x)i) Covariance matrix calculation data point xiThe normal vectors corresponding to the k neighboring points; unifying the directions of the normal vectors of the obtained fitting tangent planes, keeping the directions of the normal vectors of the adjacent planes consistent, and completing plane fitting of the data point local areas of each complete model point cloud data and the dental crown model point cloud data;
(3) curvature estimation of point cloud data
a) Respectively carrying out curvature estimation on the complete model surface point cloud data and the dental crown model surface point cloud data in the maximum bounding box L by utilizing a paraboloid fitting algorithm, wherein the main curvature of the curved surface of the complete model surface point cloud data and the main curvature of the dental crown model surface point cloud data are the same as the main curvature of the paraboloid, and the paraboloid equation is as follows:
Z＝bx2+cxy+dy2
b) let vertex x of parabolic equationlEstablishing a coordinate system of the local coordinate system and k adjacent points thereof for enabling x to be xlThe normal vector and the Z axis are merged, and the rotation is transformed into a local coordinate system of k nearest points to form a linear equation system:
AX＝Z
wherein,
X＝[b c d]T，Z＝[z1... zk]T。
c) solving a formula AX (Z) by using a Singular Value Decomposition (SVD) method to obtain coefficients b, c and d, thereby obtaining a paraboloid equation;
(4) calculating complete model point cloud data and crown model point cloud data and average curvature H by using b, c and d coefficients: h ═ b + d
And comparing the curvatures of the point cloud data of the complete model and the point cloud data of the dental crown model with a set threshold, wherein the dental crown and the dental root in the dental model are divided into the following parts according to the geometrical shape and the concave-convex property of the tooth: peaks, valleys, ridges, wherein
The peak corresponds to the cusp characteristic of the dental crown surface of the dental body, the Gaussian curvature H is greater than 0 (namely the point is an elliptic point), and the average curvature K is greater than 0 (namely the local area is convex); the valley corresponds to a groove on the dental crown surface of the tooth body, the Gaussian curvature H of the valley is greater than 0 (namely the point is an elliptic point), and the average curvature K is less than 0 (namely the local area is concave); the ridges correspond to various ridge lines on the tooth body, and the concave-convex shape is determined according to the curvature of adjacent points. Setting the corresponding peak threshold value as 50, filtering the point set with the curvature of the curved surface lower than the threshold range, keeping the point set with the curvature higher than the threshold range, setting the corresponding valley threshold value as-50, filtering the point set with the curvature of the curved surface larger than the threshold range, keeping the point set with the curvature smaller than the threshold range, reducing the data volume of the point cloud, and keeping the contour characteristics of the three-dimensional tooth body at the same time, as shown in fig. 2;
4) and performing view rendering:
manually picking up one or more characteristic points q of each tooth by using the full model point cloud data and the crown model point cloud data after down samplingjRegarding each feature point as a spherical surface, wherein j is 1.. N, N is the number of the feature points, and the radius of the spherical surface is r; the view rendering is to generate a 3D local geometric figure by projecting a specific point, select a fixed viewpoint ck to the point cloud data of the downsampled complete model and the dental crown modelCharacteristic point q of model point cloud datajPerforming perspective projection to obtain characteristic point qjGenerating a corresponding probability map DjProbability map DjComprises a plurality of output pixels, each output pixel is described by a characteristic point qjProbability of coverage, all output pixels constituting a rendering output T, the output T of the ith pixel in the rendering output TiIs defined as:
wherein C isjIs a point qjRendering attribute of, CbAs a default background value, zjIs qjDepth of (a), w (-) and wbIs a weighting function and
Djis a characteristic point qjThe probability map of the output is then calculated,
representing the probability distribution of the ith pixel,
thereby respectively obtaining a complete model rendering output and a dental crown model rendering output;
5) extracting the rendered features:
learning a local image descriptor by adopting a Convolutional Neural Network (CNN), and extracting corresponding characteristics of each tooth from the rendering output of the complete model and the rendering output of the dental crown model respectively to obtain the characteristics of the complete model and the dental crown model;
6) fusing multiple views:
the connection relation between the complete model characteristic and the same part in the dental crown model characteristic is called a group of characteristic maps, and the characteristic maps are used for mapping
As input, a soft-view pool method using sub-network adaptive weight estimation in the fusion process, the sub-network mapping F with each featurekAs input, according to the coding-decoding process, corresponding to the coding-decoding processRegression is carried out on the weight; then sub-network executes down-sampling, up-sampling the space size and channel depth more than 2 times, finally embedding the complete model characteristic and the crown model characteristic into n dimension (n)>3) Performing feature fusion on the descriptor space to obtain a fused image; wherein the n-dimensional space has a fully connected layer and a normalized layer; (ii) a
7) Constructing a loss function:
wherein,
the number of the characteristic points in the complete model point cloud data and the dental crown model point cloud data extracted after the step 4) is N, y is the corresponding point of the targetiFor estimating the corresponding point of the target, R and T are respectively a rotation transformation matrix and a translation transformation matrix, and the Loss function obtained by directly calculating the Euclidean distance is Loss1Considering that the registration task is also constrained by the global geometric transformation, another Loss including the global geometric constraint is introduced2；
The composite loss function is:
Loss＝aLoss1+(1-a)Loss2
wherein a is Loss1And Loss20.1 in between;
8) and correcting the fused image by constructing a loss function.
Finally, it is noted that the disclosed embodiments are intended to aid in further understanding of the invention, but those skilled in the art will appreciate that: various substitutions and modifications are possible without departing from the spirit and scope of the invention and the appended claims. Therefore, the invention should not be limited to the embodiments disclosed, but the scope of the invention is defined by the appended claims.
Claims (6)
1. A CBCT and laser scanning point cloud data tooth registration method based on multi-view fusion is characterized by comprising the following steps:
1) acquiring point cloud data of different sources:
extracting a tooth complete model from CBCT data according to a region growing method, converting the tooth complete model into complete model point cloud data, extracting a dental crown three-dimensional model by adopting a laser scanner, and converting the dental crown three-dimensional model into dental crown model point cloud data;
2) denoising the complete model point cloud data and the dental crown model point cloud data respectively by using a statistical outlier elimination filter to obtain denoised complete model point cloud data and dental crown model point cloud data;
3) respectively carrying out down-sampling on the denoised complete model point cloud data and the dental crown model point cloud data to reduce the point cloud data volume;
4) and performing view rendering:
manually picking up one or more characteristic points q of each tooth by using the full model point cloud data and the crown model point cloud data after down samplingjRegarding each feature point as a spherical surface, wherein j is 1.. N, N is the number of the feature points, and the radius of the spherical surface is r; selecting a fixed viewpoint ck, and performing down-sampling on the characteristic points q of the complete model point cloud data and the crown model point cloud datajPerforming perspective projection to obtain characteristic point qjGenerating a corresponding probability map DjProbability map DjComprises a plurality of output pixels, all of which constitute a rendering output T, the output T of the ith pixel in the rendering output TiComprises the following steps:
where I1.. multidot.i, I is the number of output pixels, CjIs a point qjRendering attribute of, CbAs a default background value, zjIs qjDepth of (a), w (-) and wbIs a weighting function and
Djis a characteristic point qjThe probability map of the output is then calculated,
representing the probability distribution of the ith pixel so as to respectively obtain the rendering output of the complete model and the rendering output of the dental crown model;
5) performing feature extraction on the rendering output:
learning a local image descriptor by adopting a convolutional neural network, and extracting corresponding characteristics of each tooth from the rendering output of the complete model and the rendering output of the dental crown model respectively to obtain the characteristics of the complete model and the characteristics of the dental crown model;
6) fusing multiple views:
the connection relation between the complete model characteristic and the same part in the dental crown model characteristic is called a group of characteristic maps, and the characteristic maps are used for mapping
As input, performing feature fusion, and realizing registration of the complete model feature and the dental crown model feature to obtain a fusion image;
7) constructing a loss function:
loss function by direct calculation of Euclidean distance is Loss1And Loss including global geometric constraints2The resulting composite loss function is:
Loss＝aLoss1+(1-a)Loss2
wherein a is Loss1And Loss2The weight of the intermediate weight is 0.1-0.2;
8) and correcting the fused image by constructing a loss function.
2. The registration method as claimed in claim 1, wherein in step 2), denoising the point cloud data by using a statistical outlier elimination filter, comprises the following steps:
a) calculating the average distance d from each data point in the point cloud data to the nearest k neighborhood points by adopting a k nearest neighbor algorithm, wherein k is the number of the selected neighborhood points and is set to be 1-10;
b) calculating an expected value dm and a standard deviation s of the average distance d;
c) calculating a formula for calculating the distance threshold dt according to the expected value dm and the standard deviation s,
dt＝dm+λ×s
wherein lambda is a standard deviation parameter, and the value of lambda is 0.1-0.3;
d) and comparing the average distance d of each characteristic point with a distance threshold dt, if d > dt, filtering the point, and otherwise, keeping the point.
3. The registration method according to claim 1, wherein in step 3), downsampling the denoised full model point cloud data and crown model point cloud data, respectively, comprises the steps of:
(1) search space partitioning of point cloud data
Determining the sizes of the complete model point cloud data and the crown model point cloud data in space, obtaining X, Y and the minimum and maximum values of the Z coordinate axes are Min _ x, Max _ x, Min _ y, Max _ y, Min _ Z and Max _ Z respectively, and constructing the maximum space range of the tooth point cloud: and [ Min _ x, Max _ x ] × [ Min _ y, Max _ y ] × [ Min _ z, Max _ z ], carrying out spatial segmentation on the maximum range of the tooth point cloud to obtain a maximum bounding box L of the tooth point cloud:
wherein beta is a size factor for adjusting the maximum tooth bounding box, the value is 0.5-0.8, and k is the number of selected field points and is set to be 1-10;
(2) normal vector estimation of point cloud data
Taking the normal vector of the data point of each complete model point cloud data and the dental crown model point cloud data in the maximum bounding box L as the normal vector of the fitting tangent plane of the data point, calculating the fitting tangent plane of each data point, and finishing the plane fitting of the local area of the data point of each complete model point cloud data and the dental crown model point cloud data;
(3) curvature estimation of point cloud data
a) Respectively carrying out curvature estimation on the complete model surface point cloud data and the dental crown model surface point cloud data in the maximum bounding box L by utilizing a paraboloid fitting algorithm, wherein the main curvature of the curved surface of the complete model surface point cloud data and the main curvature of the dental crown model surface point cloud data are the same as the main curvature of the paraboloid, and the paraboloid equation is as follows:
Z＝bx2+cxy+dy2
b) let vertex x of parabolic equationlEstablishing a coordinate system of the local coordinate system and k adjacent points thereof for enabling x to be xlThe normal vector and the Z axis are merged, and the rotation is transformed into a local coordinate system of k nearest points to form a linear equation system:
AX＝Z
wherein,
X＝[b c d]T，Z＝[z1… zk]T
c) solving a formula AX (Z) by using a Singular Value Decomposition (SVD) method to obtain coefficients b, c and d, thereby obtaining a paraboloid equation;
(4) calculating the average curvature H of the complete model point cloud data and the dental crown model point cloud data by using the b, c and d coefficients:
H＝b+d
comparing the average curvature of the point cloud data of the complete model and the point cloud data of the dental crown model with a set threshold, and dividing the dental crown and the dental root in the point cloud data into: peaks, valleys and ridges, wherein the peaks correspond to cusp characteristics of the dental crown surface of the tooth body, the mean curvature H > 0; the valley corresponds to the sulcus on the crown surface of the tooth body, and the average curvature H is less than 0; the ridges correspond to various ridge lines on the tooth body, and the concave-convex shape is determined according to the curvature of adjacent points; setting a corresponding peak threshold value to be 30-50, and filtering point cloud data of which the average curvature of a peak is lower than the peak threshold value range in the point cloud data; and setting the corresponding valley threshold interval to be-50 to-30, and filtering the point cloud data of which the average curvature of the valleys is larger than the valley threshold range in the point cloud data.
4. The registration method according to claim 3, wherein in step (2), k neighboring points of the data point x are denoted as Nb (x), n is a normal vector of the data point x, the normal vector n of the data point is a normal vector of a fitting tangent plane of the data point, and the fitting tangent plane Tp (x) is obtained by a least square methodi) Calculating covariance matrixes of k neighbor points Nb (x), and calculating normal vectors corresponding to the k neighbor points of the data point x according to the covariance matrixes of Nb (x); and unifying the directions of the normal vectors of the obtained fitting tangent planes, keeping the directions of the normal vectors of the adjacent planes consistent, and finishing the plane fitting of the data point local areas of each complete model point cloud data and the dental crown model point cloud data.
5. The registration method according to claim 1, wherein in step 6), a soft-view pool method of sub-network adaptive weight estimation is used in the fusion process, the sub-network uses each feature map Fk as input, and regresses the corresponding weight according to the encoding-decoding process; then, performing down-sampling by a sub-network, performing up-sampling on the space size and the channel depth by more than 2 times, and finally embedding the complete model characteristic and the crown model characteristic into an n-dimensional descriptor space for characteristic fusion to obtain a fusion image; wherein the n-dimensional space has a fully connected layer and a normalized layer, n > 3.
6. Registration method according to claim 1, characterized in that in step 7) the Loss function derived by direct calculation of the euclidean distance is Loss1Loss of including global geometric constraint2Respectively as follows:
wherein,
the number of the characteristic points in the complete model point cloud data and the dental crown model point cloud data extracted after the step 4) is N, y is the corresponding point of the targetiFor estimating the target corresponding point, R is a rotation transformation matrix, and T is a translation transformation matrix.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010772778.XA CN111862171B (en) | 2020-08-04 | 2020-08-04 | CBCT and laser scanning point cloud data tooth registration method based on multi-view fusion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010772778.XA CN111862171B (en) | 2020-08-04 | 2020-08-04 | CBCT and laser scanning point cloud data tooth registration method based on multi-view fusion |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111862171A true CN111862171A (en) | 2020-10-30 |
| CN111862171B CN111862171B (en) | 2021-04-13 |
Family
ID=72953274
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010772778.XA Active CN111862171B (en) | 2020-08-04 | 2020-08-04 | CBCT and laser scanning point cloud data tooth registration method based on multi-view fusion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111862171B (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112614166A (en) * | 2020-12-11 | 2021-04-06 | 北京影谱科技股份有限公司 | Point cloud matching method and device based on CNN-KNN |
| CN112687010A (en) * | 2021-01-08 | 2021-04-20 | 中国计量大学 | Digital metering method for end frame drill jig |
| CN112967219A (en) * | 2021-03-17 | 2021-06-15 | 复旦大学附属华山医院 | Two-stage dental point cloud completion method and system based on deep learning network |
| CN112991273A (en) * | 2021-02-18 | 2021-06-18 | 山东大学 | Automatic detection method and system for orthodontic characteristics of three-dimensional tooth model |
| CN113077502A (en) * | 2021-04-16 | 2021-07-06 | 中南大学 | Dental space registration method based on multi-layer spherical surface generation points in marker sphere |
| CN113393568A (en) * | 2021-06-08 | 2021-09-14 | 先临三维科技股份有限公司 | Training method, device, equipment and medium for neck-edge linear deformation prediction model |
| CN113628177A (en) * | 2021-07-29 | 2021-11-09 | 北京好运达智创科技有限公司 | Double-layer beam storage detection system for beam body |
| CN113657387A (en) * | 2021-07-07 | 2021-11-16 | 复旦大学 | Semi-supervised three-dimensional point cloud semantic segmentation method based on neural network |
| WO2022218000A1 (en) * | 2021-04-13 | 2022-10-20 | 杭州朝厚信息科技有限公司 | Method for generating three-dimensional digital model representing dentition in target tooth arrangement |
| CN115830287A (en) * | 2023-02-20 | 2023-03-21 | 汉斯夫(杭州)医学科技有限公司 | Tooth point cloud fusion method, equipment and medium based on laser oral scanning and CBCT reconstruction |
| WO2023087526A1 (en) * | 2021-11-18 | 2023-05-25 | 上海仙途智能科技有限公司 | Point cloud denoising method, electronic device, and storage medium |
Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103860191A (en) * | 2012-12-14 | 2014-06-18 | 奥姆科公司 | Integration of intra-oral imagery and volumetric imagery |
| CN105447908A (en) * | 2015-12-04 | 2016-03-30 | 山东山大华天软件有限公司 | Dentition model generation method based on oral cavity scanning data and CBCT (Cone Beam Computed Tomography) data |
| CN105596106A (en) * | 2015-12-17 | 2016-05-25 | 中国人民解放军第四军医大学 | Method for acquisition of complete dentition model by computer rectification combination by sectionally taking model |
| CN105662608A (en) * | 2015-12-18 | 2016-06-15 | 北京大学口腔医学院 | Three-dimensional data tooth crown and root integrating method |
| CN106295170A (en) * | 2016-08-08 | 2017-01-04 | 西安科技大学 | Tooth modeling method based on corona Yu root of the tooth feature |
| CN106327535A (en) * | 2016-08-16 | 2017-01-11 | 苏州迪凯尔医疗科技有限公司 | CBCT tooth root and intraoral scanning dental crown fusion method |
| CN107707755A (en) * | 2017-09-28 | 2018-02-16 | 努比亚技术有限公司 | Button application method, terminal and computer-readable recording medium |
| CN108665533A (en) * | 2018-05-09 | 2018-10-16 | 西安增材制造国家研究院有限公司 | A method of denture is rebuild by tooth CT images and 3 d scan data |
| CN108765474A (en) * | 2018-04-17 | 2018-11-06 | 天津工业大学 | A kind of efficient method for registering for CT and optical scanner tooth model |
| CN109712625A (en) * | 2019-02-18 | 2019-05-03 | 珠海格力电器股份有限公司 | Intelligent equipment control method and system based on gateway and intelligent gateway |
| EP3503038A1 (en) * | 2017-12-22 | 2019-06-26 | Promaton Holding B.V. | Automated 3d root shape prediction using deep learning methods |
| CN110264504A (en) * | 2019-06-28 | 2019-09-20 | 北京国润健康医学投资有限公司 | A kind of three-dimensional registration method and system for augmented reality |
| CN110443842A (en) * | 2019-07-24 | 2019-11-12 | 大连理工大学 | Depth map prediction technique based on visual angle fusion |
| EP3620130A1 (en) * | 2018-09-04 | 2020-03-11 | Promaton Holding B.V. | Automated orthodontic treatment planning using deep learning |
| CN111144175A (en) * | 2018-11-05 | 2020-05-12 | 杭州海康威视数字技术股份有限公司 | Image detection method and device |
| CN111274954A (en) * | 2020-01-20 | 2020-06-12 | 河北工业大学 | Embedded platform real-time falling detection method based on improved attitude estimation algorithm |
| CN111325279A (en) * | 2020-02-26 | 2020-06-23 | 福州大学 | Pedestrian and personal sensitive article tracking method fusing visual relationship |
| CN111369539A (en) * | 2020-03-06 | 2020-07-03 | 浙江大学 | Building facade window detecting system based on multi-feature map fusion |
| CN111415419A (en) * | 2020-03-19 | 2020-07-14 | 西安知北信息技术有限公司 | Method and system for making tooth restoration model based on multi-source image |
-
2020
- 2020-08-04 CN CN202010772778.XA patent/CN111862171B/en active Active
Patent Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103860191A (en) * | 2012-12-14 | 2014-06-18 | 奥姆科公司 | Integration of intra-oral imagery and volumetric imagery |
| CN105447908A (en) * | 2015-12-04 | 2016-03-30 | 山东山大华天软件有限公司 | Dentition model generation method based on oral cavity scanning data and CBCT (Cone Beam Computed Tomography) data |
| CN105596106A (en) * | 2015-12-17 | 2016-05-25 | 中国人民解放军第四军医大学 | Method for acquisition of complete dentition model by computer rectification combination by sectionally taking model |
| CN105662608A (en) * | 2015-12-18 | 2016-06-15 | 北京大学口腔医学院 | Three-dimensional data tooth crown and root integrating method |
| CN106295170A (en) * | 2016-08-08 | 2017-01-04 | 西安科技大学 | Tooth modeling method based on corona Yu root of the tooth feature |
| CN106327535A (en) * | 2016-08-16 | 2017-01-11 | 苏州迪凯尔医疗科技有限公司 | CBCT tooth root and intraoral scanning dental crown fusion method |
| CN107707755A (en) * | 2017-09-28 | 2018-02-16 | 努比亚技术有限公司 | Button application method, terminal and computer-readable recording medium |
| EP3503038A1 (en) * | 2017-12-22 | 2019-06-26 | Promaton Holding B.V. | Automated 3d root shape prediction using deep learning methods |
| CN108765474A (en) * | 2018-04-17 | 2018-11-06 | 天津工业大学 | A kind of efficient method for registering for CT and optical scanner tooth model |
| CN108665533A (en) * | 2018-05-09 | 2018-10-16 | 西安增材制造国家研究院有限公司 | A method of denture is rebuild by tooth CT images and 3 d scan data |
| EP3620130A1 (en) * | 2018-09-04 | 2020-03-11 | Promaton Holding B.V. | Automated orthodontic treatment planning using deep learning |
| CN111144175A (en) * | 2018-11-05 | 2020-05-12 | 杭州海康威视数字技术股份有限公司 | Image detection method and device |
| CN109712625A (en) * | 2019-02-18 | 2019-05-03 | 珠海格力电器股份有限公司 | Intelligent equipment control method and system based on gateway and intelligent gateway |
| CN110264504A (en) * | 2019-06-28 | 2019-09-20 | 北京国润健康医学投资有限公司 | A kind of three-dimensional registration method and system for augmented reality |
| CN110443842A (en) * | 2019-07-24 | 2019-11-12 | 大连理工大学 | Depth map prediction technique based on visual angle fusion |
| CN111274954A (en) * | 2020-01-20 | 2020-06-12 | 河北工业大学 | Embedded platform real-time falling detection method based on improved attitude estimation algorithm |
| CN111325279A (en) * | 2020-02-26 | 2020-06-23 | 福州大学 | Pedestrian and personal sensitive article tracking method fusing visual relationship |
| CN111369539A (en) * | 2020-03-06 | 2020-07-03 | 浙江大学 | Building facade window detecting system based on multi-feature map fusion |
| CN111415419A (en) * | 2020-03-19 | 2020-07-14 | 西安知北信息技术有限公司 | Method and system for making tooth restoration model based on multi-source image |
Non-Patent Citations (3)
| Title |
|---|
| ZHIMING CUI ET AL.: "ToothNet: Automatic Tooth Instance Segmentation and Identification From Cone Beam CT Images", 《2019IEEE/CVPR》 * |
| 张东霞等: "基于口腔计算机断层扫描图像与激光扫描图", 《生物医学工程杂志》 * |
| 张雅玲等: "基于GCNN 的CBCT 模拟口扫点云数据牙齿分割算法", 《计算机辅助设计与图形学学报》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112614166A (en) * | 2020-12-11 | 2021-04-06 | 北京影谱科技股份有限公司 | Point cloud matching method and device based on CNN-KNN |
| CN112687010A (en) * | 2021-01-08 | 2021-04-20 | 中国计量大学 | Digital metering method for end frame drill jig |
| CN112687010B (en) * | 2021-01-08 | 2024-03-15 | 中国计量大学 | Digital metering method of end frame drilling jig |
| CN112991273A (en) * | 2021-02-18 | 2021-06-18 | 山东大学 | Automatic detection method and system for orthodontic characteristics of three-dimensional tooth model |
| CN112967219A (en) * | 2021-03-17 | 2021-06-15 | 复旦大学附属华山医院 | Two-stage dental point cloud completion method and system based on deep learning network |
| CN112967219B (en) * | 2021-03-17 | 2023-12-05 | 复旦大学附属华山医院 | Two-stage dental point cloud completion method and system based on deep learning network |
| WO2022218000A1 (en) * | 2021-04-13 | 2022-10-20 | 杭州朝厚信息科技有限公司 | Method for generating three-dimensional digital model representing dentition in target tooth arrangement |
| CN113077502A (en) * | 2021-04-16 | 2021-07-06 | 中南大学 | Dental space registration method based on multi-layer spherical surface generation points in marker sphere |
| CN113393568A (en) * | 2021-06-08 | 2021-09-14 | 先临三维科技股份有限公司 | Training method, device, equipment and medium for neck-edge linear deformation prediction model |
| CN113657387B (en) * | 2021-07-07 | 2023-10-13 | 复旦大学 | Semi-supervised three-dimensional point cloud semantic segmentation method based on neural network |
| CN113657387A (en) * | 2021-07-07 | 2021-11-16 | 复旦大学 | Semi-supervised three-dimensional point cloud semantic segmentation method based on neural network |
| CN113628177A (en) * | 2021-07-29 | 2021-11-09 | 北京好运达智创科技有限公司 | Double-layer beam storage detection system for beam body |
| WO2023087526A1 (en) * | 2021-11-18 | 2023-05-25 | 上海仙途智能科技有限公司 | Point cloud denoising method, electronic device, and storage medium |
| CN115830287B (en) * | 2023-02-20 | 2023-12-12 | 汉斯夫(杭州)医学科技有限公司 | Tooth point cloud fusion method, device and medium based on laser mouth scanning and CBCT reconstruction |
| CN115830287A (en) * | 2023-02-20 | 2023-03-21 | 汉斯夫(杭州)医学科技有限公司 | Tooth point cloud fusion method, equipment and medium based on laser oral scanning and CBCT reconstruction |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111862171B (en) | 2021-04-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111862171B (en) | CBCT and laser scanning point cloud data tooth registration method based on multi-view fusion | |
| CN112200843B (en) | Super-voxel-based CBCT and laser scanning point cloud data tooth registration method | |
| US11735306B2 (en) | Method, system and computer readable storage media for creating three-dimensional dental restorations from two dimensional sketches | |
| JP7493464B2 (en) | Automated canonical pose determination for 3D objects and 3D object registration using deep learning | |
| Zanjani et al. | Mask-MCNet: Tooth instance segmentation in 3D point clouds of intra-oral scans | |
| CN112085821A (en) | Semi-supervised-based CBCT (cone beam computed tomography) and laser scanning point cloud data registration method | |
| CN106504331B (en) | Tooth modeling method based on three-dimensional model retrieval | |
| CN115619773B (en) | Three-dimensional tooth multi-mode data registration method and system | |
| WO2001010339A2 (en) | Method and apparatus for producing an implant | |
| CN111685899A (en) | Dental orthodontic treatment monitoring method based on intraoral images and three-dimensional models | |
| CN112614169B (en) | 2D/3D spine CT (computed tomography) level registration method based on deep learning network | |
| US20210103756A1 (en) | System and method for segmenting normal organ and/or tumor structure based on artificial intelligence for radiation treatment planning | |
| CN111968146A (en) | Three-dimensional tooth jaw mesh model segmentation method | |
| CN112785609B (en) | CBCT tooth segmentation method based on deep learning | |
| CN112308895A (en) | Method for constructing realistic dentition model | |
| CN116205956A (en) | Point cloud registration method and device and medical imaging equipment | |
| CN116958437A (en) | Multi-view reconstruction method and system integrating attention mechanism | |
| WO2024109845A1 (en) | 2d-3d image registration method and system | |
| CN115830163A (en) | Progressive medical image cross-mode generation method and device based on deterministic guidance of deep learning | |
| Ben-Hamadou et al. | 3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge | |
| CN107146232A (en) | The data fusion method of oral cavity CBCT images and laser scanning tooth mesh | |
| CN116407080A (en) | Evolution identification and 3D visualization system and method for fundus structure of myopic patient | |
| CN114758073A (en) | Oral cavity digital system based on RGBD input and flexible registration | |
| Broll et al. | Generative deep learning approaches for the design of dental restorations: A narrative review | |
| CN113256693A (en) | Multi-view registration method based on K-means and normal distribution transformation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |