CN112120810A - Three-dimensional data generation method of tooth orthodontic concealed appliance - Google Patents
Three-dimensional data generation method of tooth orthodontic concealed appliance Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 65
- 210000004513 dentition Anatomy 0.000 claims abstract description 38
- 230000036346 tooth eruption Effects 0.000 claims abstract description 38
- 210000000214 mouth Anatomy 0.000 claims abstract description 18
- 238000007408 cone-beam computed tomography Methods 0.000 claims abstract description 15
- 210000004195 gingiva Anatomy 0.000 claims abstract description 11
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- 238000010146 3D printing Methods 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 4
- 238000007621 cluster analysis Methods 0.000 claims description 4
- 238000010801 machine learning Methods 0.000 claims description 4
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- 108090000623 proteins and genes Proteins 0.000 claims description 4
- 238000010104 thermoplastic forming Methods 0.000 claims description 4
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- 238000013507 mapping Methods 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000003825 pressing Methods 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 2
- 210000001847 jaw Anatomy 0.000 description 8
- 238000004519 manufacturing process Methods 0.000 description 4
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61C—DENTISTRY; APPARATUS OR METHODS FOR ORAL OR DENTAL HYGIENE
- A61C7/00—Orthodontics, i.e. obtaining or maintaining the desired position of teeth, e.g. by straightening, evening, regulating, separating, or by correcting malocclusions
- A61C7/002—Orthodontic computer assisted systems
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/03—Computed tomography [CT]
- A61B6/032—Transmission computed tomography [CT]
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
- A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
- A61B6/51—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for dentistry
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61C—DENTISTRY; APPARATUS OR METHODS FOR ORAL OR DENTAL HYGIENE
- A61C7/00—Orthodontics, i.e. obtaining or maintaining the desired position of teeth, e.g. by straightening, evening, regulating, separating, or by correcting malocclusions
- A61C7/08—Mouthpiece-type retainers or positioners, e.g. for both the lower and upper arch
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Abstract
The invention discloses a three-dimensional data generation method of a tooth orthodontic concealed appliance, which comprises the following steps: s1, acquiring a CT image of the skull of the patient through CBCT, and acquiring complete image data of all teeth and upper and lower jaws in the oral cavity of the patient; s2, accurately dividing crowns, tooth roots and gingiva of upper and lower teeth in the CT image of the patient and generating dentition point cloud grid model data; s3, planning tooth arrangement of upper and lower dentitions based on dentition point cloud grid model data, and taking arranged dental jaw data as three-dimensional point cloud grid model data of the appliance.
Description
Technical Field
The invention relates to the technical field of tooth orthodontics, in particular to a three-dimensional data generation method of a tooth orthodontics concealed appliance.
Background
With the increasing importance of people on teeth, methods for making teeth beautiful and tidy through treatments such as protection, straightening and whitening of teeth are becoming more popular. At present, tooth orthodontics is vigorously developed in the beauty industry, and people can obtain regular dental arch shapes through tooth orthodontics. In the field of oral medicine, there are two types of appliances, one is a dentition appliance and the other is a concealed appliance. For a user, the dentition appliance is not beautiful enough, is not easy to clean, is easy to scratch the oral cavity of the user, cannot be taken down during correction, and is easy to breed bacterial plaque to cause tooth discoloration. The hidden appliance is beautiful and easy to clean, is attached to the teeth of a user, does not scratch the oral cavity, can be taken and worn by oneself, and is more popular with the user.
At present, the manufacture of the concealed appliance needs to acquire the data of the dental crown and the gingival surface of a patient and the data of the dental root and the jaw bone inside the oral cavity respectively through an oral cavity scanner and a cone beam computed tomography device CBCT. And then registering the multi-source data to a uniform coordinate system, extracting a complete dentition triangular mesh model by fusion modes such as cutting, splicing and the like, adjusting dentition by a dental technician according to clinical requirements, generating a dental model according to the arranged dentition by software, and finally manufacturing a corresponding appliance according to the dental model.
The oral cavity scanning and registering are time-consuming processes, a doctor needs to stretch a scanning probe into the oral cavity of a patient to scan teeth one by one so as to obtain surface data of crowns and gingiva and construct a triangular mesh model, and the process lasts for a long time and is easy to cause discomfort of the patient. Meanwhile, the probe is extended into the oral cavity, and although the probe is subjected to disinfection treatment, the risk of cross infection also exists. Moreover, the traditional CBCT is considered to have low extraction accuracy for dental crowns, so a dental crown data needs to be acquired by using an oral cavity scanner, and the dentition model acquired after registration is more accurate. However, because the three-dimensional data is constructed by using different coordinate systems, the whole dentition triangular mesh model can be accurately obtained only after the three-dimensional data needs to be uniformly registered. The registration process requires manual selection of anatomical feature points on the teeth, which introduces new errors due to the inaccurate selection, resulting in a deviation of the dentition model.
Therefore, the three-dimensional data of the orthodontic concealed appliance can be generated only by a single CBCT data source, and a data base is laid for the appliance to be generated quickly by the 3D printing technology. The technical problem to be solved by the invention is solved.
Disclosure of Invention
The invention aims to provide a method for generating three-dimensional data of a hidden orthodontic appliance based on Cone Beam Computed Tomography (CBCT), which is used for generating triangular grid data of the appliance and laying a data foundation for the rapid forming of the appliance. The problem that the dentition structure can be constructed only by multi-source data in the prior art is solved.
The invention relates to a data generation method of a tooth appliance, in particular to a three-dimensional data generation method of a tooth orthodontic concealed appliance. Is mainly applied to clinical medicine such as orthodontic treatment, oral implantation and the like.
The invention provides a three-dimensional data generation method of a tooth orthodontic concealed appliance, which comprises the following steps:
s1, acquiring a CT image of the skull of the patient through cone-beam computed tomography equipment, and acquiring complete image data of all teeth and upper and lower jaws in the oral cavity of the patient;
s2, accurately dividing crowns, tooth roots and gingiva of upper and lower teeth in the CT image of the patient and generating dentition point cloud grid model data;
and S3, planning the tooth arrangement of the upper and lower dentitions based on the dentition point cloud grid model data, and taking the arranged dental data as the three-dimensional point cloud grid model data of the appliance.
In a further improvement, the step S1 specifically includes:
s11, acquiring a CT image of the skull of the patient through cone-beam computed tomography equipment, and preprocessing the CT image;
s12, carrying out maximum gray projection on the oral cavity CT image along the coronal plane direction to obtain a coronal maximum gray projection image, and then executing a threshold operation to generate a coronal binary image;
s13, horizontally projecting the coronal binary image, selecting a projection interval with a maximum peak value and continuous pixel distribution as a layer number range of a transverse plane containing a tooth area, and obtaining a transverse plane maximum gray projection image based on the detected layer number range;
s14, selecting a threshold value of image binarization according to the gray standard deviation range of the maximum gray projection image of the cross section, and then determining the range of the tooth area of the cross section by defining the range of the tooth pixels in the binary image, thereby obtaining the complete image data of all teeth and upper and lower jaws of the patient in the oral cavity.
In a further improvement, the step S2 specifically includes:
s21, learning the preprocessed CT images of the upper and lower teeth and the jawbone and the corresponding clinical tooth golden standard by adopting a full convolution network, continuously carrying out iterative updating of the network, and finally obtaining a tooth probability graph capable of predicting the mapping relation between the CT images and the golden standard;
s22, binarizing the tooth probability map by a threshold method, then searching a connected region in the image and performing morphological expansion to obtain a binary tooth image;
s23, generating a foreground mark and a background mark according to the binary tooth image, calculating the gradient of the tooth probability map, fusing the gradient with the tooth surface probability map to obtain an input image with watershed transformation, and marking the watershed transformation to realize automatic segmentation of a single tooth by taking the foreground mark and the background mark as guidance;
and S24, evaluating the segmentation effect of the single tooth, and feeding back the learning of the full convolution network to optimize the learning effect of the network, improve the accuracy of tooth segmentation and further generate the dentition point cloud grid model data.
The further improvement is that the foreground mark is obtained by using a morphological opening operation, the background mark is obtained by performing Euclidean distance transformation on the binary image to obtain a distance map, and then performing watershed transformation on the distance map.
In a further improvement, the evaluation of the effect of segmenting a single tooth in step S24 is performed by using the following 4 indexes: jaccard coefficient, Dice similarity coefficient, relative volume difference, and average symmetric surface distance.
In a further improvement, in the step S2, the crowns, roots, and gingiva of the upper and lower teeth in the CT image of the patient are accurately segmented, and the image segmentation algorithm includes one or more of the following: threshold-based segmentation methods, region-based segmentation methods, edge-based segmentation methods, histogram methods, theory-specific based segmentation methods, machine learning, and artificial neural network methods.
The further improvement lies in that the segmentation method based on the specific theory is used for cluster analysis, fuzzy set theory, gene coding and wavelet transformation.
The method is further improved in that the parts such as crowns, roots, gingiva and the like of upper and lower teeth in CT data of a patient are accurately segmented through an image segmentation technology, triangular surface mesh model data are respectively generated by adopting a moving cube algorithm and finally combined to form dentition point cloud mesh model data.
In a further improvement, the step S3 specifically includes:
s31, adjusting three-dimensional point cloud grid models of upper and lower teeth of a patient based on the dentition point cloud grid model data, planning a tooth correction scheme of the patient, taking corrected dental jaw data as male die data of a corrector, and generating a real object of the dentition model through a 3D printing technology;
and S32, pressing and forming the orthodontic concealed appliance by using the dentition model as a template by using a thermoplastic forming technology.
Compared with the prior art, the invention has the beneficial effects that:
the invention accurately segments the anatomical structures of various parts such as dental crowns, dental roots, gingiva and the like in the oral cavity of a patient based on the image data of the CBCT and in combination with a deep learning method, generates three-dimensional data, avoids secondary reconstruction and space registration of an oral scanner, greatly shortens the chair-side operation time of a doctor, can obtain an ideal dentition three-dimensional structure by the doctor only by designing a correction scheme, can directly obtain a real object of the correction appliance in combination with 3D printing and thermoplastic molding technologies, and greatly improves the manufacturing efficiency of the correction appliance.
Drawings
The drawings are for illustrative purposes only and are not to be construed as limiting the patent; for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
FIG. 1 is a schematic overall flow chart of an embodiment of the present invention;
FIG. 2 is a flowchart illustrating the procedure and results of detecting valid tooth regions according to one embodiment of the present invention;
FIG. 3 is a schematic diagram of an image generated by performing a watershed transform according to an embodiment of the invention;
fig. 4 is a diagram illustrating the results of crown and root segmentation according to an embodiment of the present invention.
Detailed Description
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted" and "connected" are to be interpreted broadly, e.g., as being either fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, so to speak, as communicating between the two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art. The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Referring to fig. 1 and 2, a method for generating three-dimensional data of an orthodontic concealed appliance includes the following steps:
s1, acquiring a CT image of the skull of the patient through cone-beam computed tomography equipment, and acquiring complete image data of all teeth and upper and lower jaws in the oral cavity of the patient;
s2, accurately dividing crowns, tooth roots and gingiva of upper and lower teeth in the CT image of the patient and generating dentition point cloud grid model data;
and S3, planning the tooth arrangement of the upper and lower dentitions based on the dentition point cloud grid model data, and taking the arranged dental data as the three-dimensional point cloud grid model data of the appliance.
As a preferred embodiment of the present invention, the step S1 specifically includes:
and S11, acquiring CT image data of the upper and lower teeth and the upper and lower jawbones of the patient by using cone beam computed tomography equipment. In the embodiment, in order to extract a Region of interest (ROI), the invention detects and cuts the effective Region of teeth in the exit cavity CT image through threshold operation and maximum gray projection analysis for preprocessing of image segmentation;
s12, performing a detection process as shown in FIG. 2, performing maximum gray projection on the oral cavity CT image along the coronal plane direction to obtain a coronal maximum gray projection image, and performing a threshold operation to generate a coronal binary image;
s13, projecting the binary image horizontally, and selecting the projection region with the maximum peak and continuous pixel distribution as the range of the layer number whose cross section contains the tooth region (as shown by the cyan dotted line in the second sub-image of fig. 2). Based on the detected layer number range, a cross section maximum gray level projection image can be obtained;
and S14, selecting a threshold value for image binarization according to the gray standard deviation range of the maximum gray projection image of the cross section. The extent of the tooth area in the cross-section is then determined by bounding the extent of the white pixels (tooth pixels) in the binary image (as indicated by the cyan dotted line in the 5 th sub-image of figure 2). Thus, CT images of the upper and lower teeth and jaw of the patient are extracted.
In a preferred embodiment of the present invention, in step S2, the target region is segmented based on the video representation of each region by using an image segmentation technique. In this embodiment, the present invention employs a tooth segmentation method based on an artificial intelligence full convolution network and a watershed transform, and therefore, the step S2 specifically includes:
s21, learning the preprocessed CT images of the upper and lower teeth and the jawbone and the corresponding clinical tooth golden standard by adopting a full convolution network, and continuously performing iterative updating of the network to finally obtain a tooth probability graph (shown in figure 3-a) capable of predicting the mapping relation between the CT images and the golden standard;
s22, binarizing the tooth probability map by a threshold method (fig. 3-b), and then searching for a connected region in the image and performing morphological expansion to obtain a binary tooth image (fig. 3-c);
and S23, generating a foreground mark and a background mark (fig. 3-e) according to the binary image, wherein the foreground mark is obtained by using a morphological opening operation, the background mark is obtained by performing Euclidean distance transformation on the binary image to obtain a distance map (fig. 3-d), and then performing watershed transformation on the distance map. And finally, calculating the gradient (shown in figure 3-g) of the tooth probability map according to the foreground mark and the background mark, and fusing the gradient with the tooth surface probability map (shown in figure 3-f) to obtain an input image of watershed transformation. Through the guidance of the foreground mark and the background mark, the mark watershed transform realizes the automatic segmentation of the single tooth (figure 3-h);
s24, 4 indexes are used for evaluating the segmentation effect of the single tooth, and the 4 indexes are respectively: according to the indexes, the learning of the network is fed back to optimize the learning effect of the network and improve the accuracy of tooth segmentation.
To this end, the patient's overall tooth structure has been segmented and the results of the three-dimensional segmentation for the crowns and roots are shown in FIG. 4. Specifically, the image segmentation algorithm involved in step S2 may include some single specific method, or may be a combination of several methods and optimizes the obtained result.
As a preferred embodiment of the present invention, the foreground marker is obtained by using a morphological opening operation, and the background marker is obtained by performing euclidean distance transformation on the binary image to obtain a distance map, and then performing watershed transformation on the distance map.
As a preferred embodiment of the present invention, the 4 indexes are: jaccard coefficient, Dice similarity coefficient, relative volume difference, and average symmetric surface distance.
In a preferred embodiment of the present invention, in step S2, the crowns, roots, and gingiva of the upper and lower teeth in the CT image of the patient are accurately segmented, and the image segmentation algorithm includes one or more of the following: threshold-based segmentation methods, region-based segmentation methods, edge-based segmentation methods, histogram methods, theory-specific based segmentation methods, machine learning, and artificial neural network methods.
As a preferred embodiment of the invention, the segmentation method based on the specific theory comprises cluster analysis, fuzzy set theory, gene coding and wavelet transformation.
As a preferred embodiment of the invention, the parts of crowns, tooth roots, gingiva and the like of upper and lower teeth in CT data of a patient are accurately segmented by an image segmentation technology, triangular surface mesh model data are respectively generated by adopting a moving cube algorithm and are finally combined to form dentition point cloud mesh model data.
It is understood that, in the embodiment of the present invention, in step S2, the parts of the upper and lower teeth, such as the crowns, roots, and gums, are accurately segmented, the target region is segmented by the image segmentation technique according to the imaging characteristics of each part, including but not limited to the gray scale, contour, and other feature points, the related image segmentation algorithm includes but not limited to the threshold-based segmentation method, the region-based segmentation method, the edge-based segmentation method, the histogram method, the segmentation method based on specific theory (cluster analysis, fuzzy set theory, gene coding, wavelet transformation, etc.), the machine learning, the artificial neural network method, and so on, and the combination and optimization of several methods thereof, aiming at accurately extracting the specific boundary or contour of each part, and after the crowns, roots, and gums are respectively segmented into image data, further using a surface rendering three-dimensional reconstruction method, including but not limited to Marching Cubes (MC), respectively generating triangular surface mesh model data, and finally combining to form dentition model data.
As a preferred embodiment of the present invention, the step S3 specifically includes:
s31, adjusting three-dimensional point cloud grid models of upper and lower teeth of a patient based on the dentition point cloud grid model data, planning a tooth correction scheme of the patient, taking corrected dental jaw data as male die data of a corrector, and generating a real object of the dentition model through a 3D printing technology;
and S32, pressing and forming the orthodontic concealed appliance by using the dentition model as a template by using a thermoplastic forming technology.
In conclusion, the method for generating the three-dimensional data of the orthodontic appliance can acquire dentition data of the patient only by using a single CBCT data source, and a doctor can plan a tooth correction scheme of the patient according to clinical experience, so that tooth correction adjustment can be directly performed in the three-dimensional model. And finally, generating a dental jaw model entity by combining a 3D printing technology, and manufacturing the orthodontic appliance by utilizing a thermoplastic forming technology.
In the drawings, the positional relationship is described for illustrative purposes only and is not to be construed as limiting the present patent; it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (9)
1. A three-dimensional data generation method of a tooth orthodontic concealed appliance is characterized by comprising the following steps:
s1, acquiring a CT image of the skull of the patient through cone-beam computed tomography equipment, and acquiring complete image data of all teeth and upper and lower jaws in the oral cavity of the patient;
s2, accurately dividing crowns, tooth roots and gingiva of upper and lower teeth in the CT image of the patient and generating dentition point cloud grid model data;
and S3, planning the tooth arrangement of the upper and lower dentitions based on the dentition point cloud grid model data, and taking the arranged dental data as the three-dimensional point cloud grid model data of the appliance.
2. The method for generating three-dimensional data of an orthodontic concealed appliance according to claim 1, wherein the step S1 specifically comprises:
s11, acquiring a CT image of the skull of the patient through cone-beam computed tomography equipment, and preprocessing the CT image;
s12, carrying out maximum gray projection on the oral cavity CT image along the coronal plane direction to obtain a coronal maximum gray projection image, and then executing a threshold operation to generate a coronal binary image;
s13, horizontally projecting the coronal binary image, selecting a projection interval with a maximum peak value and continuous pixel distribution as a layer number range of a transverse plane containing a tooth area, and obtaining a transverse plane maximum gray projection image based on the detected layer number range;
s14, selecting a threshold value of image binarization according to the gray standard deviation range of the maximum gray projection image of the cross section, and then determining the range of the tooth area of the cross section by defining the range of the tooth pixels in the binary image, thereby obtaining the complete image data of all teeth and upper and lower jaws of the patient in the oral cavity.
3. The method for generating three-dimensional data of an orthodontic concealed appliance according to claim 1, wherein the step S2 specifically comprises:
s21, learning the preprocessed CT images of the upper and lower teeth and the jawbone and the corresponding clinical tooth golden standard by adopting a full convolution network, continuously carrying out iterative updating of the network, and finally obtaining a tooth probability graph capable of predicting the mapping relation between the CT images and the golden standard;
s22, binarizing the tooth probability map by a threshold method, then searching a connected region in the image and performing morphological expansion to obtain a binary tooth image;
s23, generating a foreground mark and a background mark according to the binary tooth image, calculating the gradient of the tooth probability map, fusing the gradient with the tooth surface probability map to obtain an input image with watershed transformation, and marking the watershed transformation to realize automatic segmentation of a single tooth by taking the foreground mark and the background mark as guidance;
and S24, evaluating the segmentation effect of the single tooth, and feeding back the learning of the full convolution network to optimize the learning effect of the network, improve the accuracy of tooth segmentation and further generate the dentition point cloud grid model data.
4. The method of claim 3, wherein the foreground marker is obtained by using a morphological opening operation, and the background marker is obtained by subjecting the binary image to Euclidean distance transform to obtain a distance map, and then subjecting the distance map to watershed transform.
5. The method for generating three-dimensional data of an orthodontic concealed appliance according to claim 3, wherein the evaluation of the segmentation effect of a single tooth in the step S24 is performed by using the following 4 indexes, wherein the 4 indexes are respectively: jaccard coefficient, Dice similarity coefficient, relative volume difference, and average symmetric surface distance.
6. The method of claim 3, wherein the step S2 is performed to precisely segment the crowns, roots and gingiva of the upper and lower teeth in the CT image of the patient, and the algorithm involved in the image segmentation comprises one or more of the following: threshold-based segmentation methods, region-based segmentation methods, edge-based segmentation methods, histogram methods, theory-specific based segmentation methods, machine learning, and artificial neural network methods.
7. The method for generating three-dimensional data of the orthodontic concealed appliance according to claim 6, wherein the segmentation method based on specific theory is cluster analysis, fuzzy set theory, gene coding and wavelet transformation.
8. The method as claimed in claim 7, wherein the image segmentation technique is used to precisely segment the crown, root, and gum of the upper and lower teeth in the CT data of the patient, and the moving cube algorithm is used to generate the triangular surface mesh model data respectively, and finally the triangular surface mesh model data are combined to form the dentition point cloud mesh model data.
9. The method for generating three-dimensional data of an orthodontic concealed appliance according to claim 1, wherein the step S3 specifically comprises:
s31, adjusting three-dimensional point cloud grid models of upper and lower teeth of a patient based on the dentition point cloud grid model data, planning a tooth correction scheme of the patient, taking corrected dental jaw data as male die data of a corrector, and generating a real object of the dentition model through a 3D printing technology;
and S32, pressing and forming the orthodontic concealed appliance by using the dentition model as a template by using a thermoplastic forming technology.
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Cited By (11)
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| 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 |
| CN113222994A (en) * | 2021-07-08 | 2021-08-06 | 北京朗视仪器股份有限公司 | Three-dimensional oral cavity model Ann's classification method based on multi-view convolutional neural network |
| CN113344950A (en) * | 2021-07-28 | 2021-09-03 | 北京朗视仪器股份有限公司 | CBCT image tooth segmentation method combining deep learning with point cloud semantics |
| CN113506301A (en) * | 2021-07-27 | 2021-10-15 | 四川九洲电器集团有限责任公司 | Tooth image segmentation method and device |
| CN113506302A (en) * | 2021-07-27 | 2021-10-15 | 四川九洲电器集团有限责任公司 | Interactive object updating method, device and processing system |
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| CN113344950A (en) * | 2021-07-28 | 2021-09-03 | 北京朗视仪器股份有限公司 | CBCT image tooth segmentation method combining deep learning with point cloud semantics |
| CN113842216A (en) * | 2021-12-01 | 2021-12-28 | 极限人工智能有限公司 | Upper and lower tooth involution simulation method and device and electronic equipment |
| CN114463407A (en) * | 2022-01-19 | 2022-05-10 | 西安交通大学口腔医院 | System for realizing oral cavity shaping simulation display by combining 3D image with feature fusion technology |
| CN114463407B (en) * | 2022-01-19 | 2023-02-17 | 西安交通大学口腔医院 | System for realizing oral cavity shaping simulation display by combining 3D image with feature fusion technology |
| WO2024082284A1 (en) * | 2022-10-21 | 2024-04-25 | 深圳先进技术研究院 | Orthodontic automatic tooth arrangement method and system based on mesh feature deep learning |
| CN115471663A (en) * | 2022-11-15 | 2022-12-13 | 上海领健信息技术有限公司 | Three-stage dental crown segmentation method, device, terminal and medium based on deep learning |
| CN115619773B (en) * | 2022-11-21 | 2023-03-21 | 山东大学 | Three-dimensional tooth multi-mode data registration method and system |
| CN115619773A (en) * | 2022-11-21 | 2023-01-17 | 山东大学 | Three-dimensional tooth multi-mode data registration method and system |
| CN116616931A (en) * | 2023-05-24 | 2023-08-22 | 广东省极数增材医疗科技有限公司 | 3D printing method for rapidly comparing tooth model with digital model |
| CN116616931B (en) * | 2023-05-24 | 2024-04-30 | 永州市鑫创艺医疗器械有限公司 | 3D printing method for rapidly comparing tooth model with digital model |
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