CN118021474A - Dental implant model forming method based on image processing - Google Patents
Dental implant model forming method based on image processing Download PDFInfo
- Publication number
- CN118021474A CN118021474A CN202410436561.XA CN202410436561A CN118021474A CN 118021474 A CN118021474 A CN 118021474A CN 202410436561 A CN202410436561 A CN 202410436561A CN 118021474 A CN118021474 A CN 118021474A
- Authority
- CN
- China
- Prior art keywords
- dental implant
- data
- oral cavity
- implant model
- oral
- 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
- 239000004053 dental implant Substances 0.000 title claims abstract description 216
- 238000000034 method Methods 0.000 title claims abstract description 56
- 238000012545 processing Methods 0.000 title claims abstract description 51
- 210000000214 mouth Anatomy 0.000 claims abstract description 185
- 238000004458 analytical method Methods 0.000 claims abstract description 78
- 230000000877 morphologic effect Effects 0.000 claims abstract description 58
- 210000000988 bone and bone Anatomy 0.000 claims abstract description 52
- 210000003781 tooth socket Anatomy 0.000 claims abstract description 42
- 230000011218 segmentation Effects 0.000 claims abstract description 36
- 238000009826 distribution Methods 0.000 claims abstract description 31
- 206010044048 Tooth missing Diseases 0.000 claims abstract description 27
- 238000000547 structure data Methods 0.000 claims abstract description 20
- 238000004364 calculation method Methods 0.000 claims abstract description 14
- 238000010276 construction Methods 0.000 claims abstract description 11
- 238000010586 diagram Methods 0.000 claims abstract description 9
- 230000008859 change Effects 0.000 claims description 67
- 239000007943 implant Substances 0.000 claims description 56
- 210000001519 tissue Anatomy 0.000 claims description 37
- 238000004088 simulation Methods 0.000 claims description 21
- 238000005457 optimization Methods 0.000 claims description 20
- 210000004872 soft tissue Anatomy 0.000 claims description 20
- 238000013461 design Methods 0.000 claims description 17
- 230000005489 elastic deformation Effects 0.000 claims description 14
- 230000001055 chewing effect Effects 0.000 claims description 13
- 230000004660 morphological change Effects 0.000 claims description 13
- 238000002513 implantation Methods 0.000 claims description 12
- 238000011002 quantification Methods 0.000 claims description 12
- 238000005070 sampling Methods 0.000 claims description 11
- 230000002093 peripheral effect Effects 0.000 claims description 7
- 238000000465 moulding Methods 0.000 abstract description 2
- 210000000515 tooth Anatomy 0.000 description 65
- 230000008569 process Effects 0.000 description 11
- 238000004422 calculation algorithm Methods 0.000 description 7
- 238000005299 abrasion Methods 0.000 description 6
- 238000013527 convolutional neural network Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 6
- 239000000463 material Substances 0.000 description 5
- 239000000284 extract Substances 0.000 description 4
- 230000003993 interaction Effects 0.000 description 4
- 230000007774 longterm Effects 0.000 description 4
- 230000002411 adverse Effects 0.000 description 3
- 238000003708 edge detection Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000003628 erosive effect Effects 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 238000011176 pooling Methods 0.000 description 3
- 238000003672 processing method Methods 0.000 description 3
- 238000012300 Sequence Analysis Methods 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 2
- 230000006399 behavior Effects 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 230000006740 morphological transformation Effects 0.000 description 2
- 238000000611 regression analysis Methods 0.000 description 2
- 230000004913 activation Effects 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000037182 bone density Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000002591 computed tomography Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000002059 diagnostic imaging Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 210000004195 gingiva Anatomy 0.000 description 1
- 238000009499 grossing 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
- 230000011164 ossification Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 238000013439 planning Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000009877 rendering Methods 0.000 description 1
- 238000007493 shaping process Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000013179 statistical model Methods 0.000 description 1
- 238000001356 surgical procedure Methods 0.000 description 1
- 238000012731 temporal analysis Methods 0.000 description 1
- 238000000700 time series analysis Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Landscapes
- Dental Tools And Instruments Or Auxiliary Dental Instruments (AREA)
Abstract
The invention relates to the field of image processing, in particular to a dental implant model forming method based on image processing. The method comprises the following steps: acquiring an oral CT image of a patient; carrying out multi-channel convolution treatment on the CT image of the oral cavity of the patient to construct an oral cavity convolution characteristic diagram; performing pixel-level semantic segmentation on the oral convolution feature map to segment the map in an oral area; generating a tooth missing region map based on the oral region segmentation map; carrying out morphological structure analysis on the tooth missing region graph to obtain tooth groove shape structure data; carrying out three-dimensional construction on the tooth socket shape structure data to generate a three-dimensional tooth socket bone model; performing topological form fitting based on the three-dimensional alveolar bone model to construct a first dental implant model; performing pressure distribution calculation around the dental implant on the first dental implant model to generate dental implant structure pressure data; the invention realizes the efficient molding of the dental implant model.
Description
Technical Field
The invention relates to the field of image processing, in particular to a dental implant model forming method based on image processing.
Background
Dental implant modeling is a common dental surgical procedure used to restore missing teeth. Traditional dental implant model forming methods generally depend on photography, impression and manual manufacturing, and have some limitations, often have lower efficiency, long time consumption, human errors and the like, and in order to meet the requirements of modern dental diagnosis and treatment, an intelligent, rapid and efficient dental implant model forming method is needed. A dental implant model forming method based on image processing has been developed.
Disclosure of Invention
The invention provides a dental implant model forming method based on image processing, which aims to solve at least one technical problem.
In order to achieve the above object, the present invention provides a dental implant model forming method based on image processing, comprising the steps of:
Step S1: acquiring an oral CT image of a patient; carrying out multi-channel convolution treatment on the CT image of the oral cavity of the patient to construct an oral cavity convolution characteristic diagram; performing pixel-level semantic segmentation on the oral convolution feature map to segment the map in an oral area;
Step S2: generating a tooth missing region map based on the oral region segmentation map; carrying out morphological structure analysis on the tooth missing region graph to obtain tooth groove shape structure data; carrying out three-dimensional construction on the tooth socket shape structure data to generate a three-dimensional tooth socket bone model;
Step S3: performing topological form fitting based on the three-dimensional alveolar bone model to construct a first dental implant model; performing pressure distribution calculation around the dental implant on the first dental implant model to generate dental implant structure pressure data;
step S4: carrying out oral cavity multi-time sequence node prediction based on dental implant structure pressure data to obtain an oral cavity multi-time point prediction result; carrying out tissue morphological structure evolution analysis on the oral cavity multi-time-point prediction result to obtain oral cavity morphological evolution prediction data;
Step S5: performing time sequence trend curve fitting on the oral cavity form evolution prediction data to obtain a form evolution time sequence trend curve; performing boundary buffer area analysis on the first dental implant model based on the morphological evolution time sequence trend curve to obtain a deformation intelligent buffer area;
step S6: performing simulated implantation on the first dental implant model based on the deformation intelligent buffer zone to obtain a holographic tooth socket-dental implant model; and carrying out stress area optimization design based on the holographic tooth socket-dental implant model to construct an optimized dental implant model.
The invention processes and segments the oral cavity CT image of the patient to obtain an accurate segmentation map of the oral cavity region, extracts key features in the oral cavity image through multichannel convolution processing to help subsequent analysis and processing, and further classifies each pixel in the oral cavity convolution feature map to enable each pixel to be accurately belonged to the oral cavity region or other regions, thereby obtaining an oral cavity region segmentation map, further analyzing and processing according to the oral cavity region segmentation map to obtain a tooth missing region map, performing morphological structure analysis on the tooth missing region map to obtain morphological structure data of tooth sockets, including shape, size, position and other information, then converting the obtained tooth socket morphological structure data into a three-dimensional tooth socket bone model through a three-dimensional construction technology to facilitate subsequent processing and analysis, performing topological shape fitting by utilizing the three-dimensional tooth socket bone model, performing dental implant surrounding pressure distribution calculation on the first tooth socket bone model according to obtain dental implant structural pressure data, optimizing the oral cavity state of the oral cavity environment, providing a predicted evolution state according to the oral cavity structural development point, and the predicted evolution point, and providing the predicted state of the oral cavity with optimal state, and providing the predicted state of the oral cavity with the predicted state of the oral cavity structure, and optimizing the oral cavity state of the oral cavity structure according to the predicted state of the predicted state, thereby obtaining the predicted state of the oral cavity state, taking into consideration changes of oral morphology, providing a certain fault-tolerant space to adapt to future changes, improving stability and adaptability of the dental implant, utilizing a deformation intelligent buffer zone to carry out simulation implantation on a first dental implant model into the oral model to obtain a complete holographic tooth socket-dental implant model, taking the trend of oral morphology evolution into consideration, providing a buffer zone adapting to future changes, carrying out stress area optimization design based on the holographic tooth socket-dental implant model, determining the stress area in the dental implant model by analyzing the stress distribution in the oral cavity, carrying out optimization design, improving the stability and adaptability of the dental implant, and ensuring long-term success of the dental implant in the oral environment.
Preferably, step S1 comprises the steps of:
step S11: acquiring an oral CT image of a patient;
step S12: carrying out multi-channel convolution processing on the CT image of the oral cavity of the patient so as to extract cross-band characteristic data;
Step S13: constructing an oral convolution feature map based on the cross-band feature data;
step S14: carrying out branch sampling treatment on the oral cavity convolution characteristic map to obtain a space detail convolution map;
Step S15: and carrying out pixel-level semantic segmentation on the space detail convolution map to segment the map of the oral cavity region.
The invention provides detailed information of oral structures, including the shapes and positions of teeth, alveolar bones and surrounding tissues of a patient by acquiring the CT images of the oral cavity, providing basic data for subsequent processing and analysis, extracting cross-band characteristic data from the CT images of the oral cavity by utilizing a multi-channel convolution processing technology, capturing correlation and characteristic representation among different bands by multi-channel convolution processing, facilitating the extraction of richer and more accurate characteristic information of the oral cavity images, wherein the oral convolution characteristic image is obtained by carrying out convolution operation on the cross-band characteristic data, better represents the characteristic information of the structures, textures, morphologies and the like in the oral cavity images, and further highlights space detail information in the oral cavity images, such as the edges, the shapes, the microstructures and the like of the teeth by branch sampling processing, facilitating the subsequent semantic segmentation and morphological analysis, classifying each pixel in the oral cavity convolution characteristic image by pixel-level semantic segmentation, accurately attributing the pixel to an oral cavity area or other area, thereby obtaining an oral cavity area segmentation image, providing accurate boundary and segmentation result of each structure in the oral cavity, and providing a basic model for subsequent dental implant molding.
Preferably, the specific steps of step S14 are:
Step S141: performing multi-layer downsampling on the oral cavity convolution feature map to obtain global large-granularity feature data;
step S142: performing multi-scale up-sampling on the oral cavity convolution feature map so as to obtain local detail feature data;
step S143: residual connection processing is carried out on the global large-granularity characteristic data and the local detail characteristic data so as to generate missing detail characteristic data;
Step S144: based on the missing detail feature data, a space detail convolution graph is obtained.
According to the invention, the dimension reduction processing is carried out on the oral cavity convolution characteristic map through multi-layer downsampling operation, so that global large-granularity characteristic data is obtained, the space size of the characteristic map is gradually reduced through multi-layer downsampling, and meanwhile, the number of channels is increased, so that the model can better capture the characteristic information of the whole oral cavity structure, and the calculation complexity is reduced, the oral cavity convolution characteristic map is amplified through multi-scale upsampling operation, so that local detail characteristic data is obtained, the space detail information of the characteristic map is restored through multi-scale upsampling, the model can better capture the characteristics of local microstructure, texture and the like in the oral cavity image, the model is facilitated to be improved in fineness and accuracy, the global large-granularity characteristic data and the local detail characteristic data are fused through residual connection processing, so that missing detail characteristic data are generated, the residual connection effectively transmits and fuses the characteristic information of different scales, so that the information loss between the global and local hierarchy is filled, the perception capability of the model on the detail information is improved, the final space detail characteristic map is obtained through integrating the global large-granularity characteristic data, the local convolution characteristic data and the missing characteristic data, the space detail information is better captured, and the detail profile is provided for the overall detail-forming detail profile and the detail profile, and the detail profile is more accurate and full-detail information is obtained.
Preferably, the specific steps of step S2 are:
step S21: carrying out fine grain structure identification on the oral area segmentation map to obtain a tooth missing area map;
Step S22: extracting the tooth socket profile of the tooth missing region graph to obtain tooth socket region profile data;
Step S23: carrying out morphological structure analysis on the outline data of the tooth socket area to obtain tooth socket shape structure data;
step S24: performing tooth topology gap calculation on the tooth missing region map to obtain peripheral tooth gap range data;
Step S25: and carrying out three-dimensional construction on the tooth socket structural data based on the peripheral tooth gap range data so as to generate a three-dimensional tooth socket bone model.
The method comprises the steps of carrying out fine-grained structure identification on an oral cavity region segmentation map to accurately position and identify missing tooth regions, analyzing and identifying the oral cavity region segmentation map to judge which regions correspond to missing tooth positions, generating a missing tooth region map, processing the missing tooth region map, extracting outline information of tooth grooves, carrying out edge detection and outline extraction on the missing tooth region map to obtain shape and boundary information of tooth grooves, obtaining outline data of tooth groove regions, carrying out morphological analysis on the outline data of tooth groove regions to obtain morphological structure information of tooth grooves, carrying out morphological transformation on the outline of tooth groove regions by applying morphological operations such as expansion, erosion, open operation or closed operation, further analyzing the shape, size and structural characteristics of the tooth grooves to obtain morphological structure data of tooth grooves, processing the missing tooth region map, calculating the topological gaps of teeth, namely the gap ranges of tooth bodies around the missing tooth, determining the gap ranges of tooth bodies around the missing tooth by analyzing the position and the peripheral tooth bodies in the missing tooth region map, providing positioning and design of an implant, carrying out morphological transformation on the outline data of the tooth groove region profile data, generating a three-dimensional basic model by combining the three-dimensional basic model with the dimensional model based on the dimensional model, and generating the dimensional basic model by using the gap state data of the gap data of the tooth groove profile.
Preferably, the specific steps of step S3 are:
Step S31: performing topological form fitting based on the three-dimensional alveolar bone model to construct a first dental implant model;
Step S32: performing elastic deformation dynamics constraint analysis on the first dental implant model to obtain elastic deformation dynamics constraint data;
Step S33: performing chewing wear simulation on the elastic deformation dynamics constraint data to obtain wear simulation data;
step S34: and performing pressure distribution calculation around the dental implant based on the wear simulation data to generate dental implant structure pressure data.
According to the method, the shape, the size, the position and other parameters of the implant are matched and fitted with the alveolar bone model, the first dental implant model is generated, good adaptation and stability of the implant and the alveolar bone are guaranteed, elastic deformation dynamics constraint analysis is conducted on the first dental implant model to obtain relevant constraint data, stress constraint conditions of the implant including parameters such as bearing capacity and stress distribution are determined through analysis of elastic deformation and mechanical behaviors of the implant in chewing and biting processes, elastic deformation dynamics constraint data are obtained, contact and abrasion conditions between the implant and surrounding tissues are evaluated through simulation of chewing motion and mechanical actions, abrasion simulation data are obtained, understanding of durability and service life of the implant is facilitated, pressure distribution conditions around the implant including parameters such as contact force and stress distribution are calculated through analysis of mechanical actions and contact conditions in chewing processes, dental implant structure pressure data are provided, and evaluation of mechanical interaction and stability between the implant and surrounding tissues is facilitated.
Preferably, the specific steps of step S4 are:
step S41: carrying out oral cavity multi-time sequence node prediction based on dental implant structure pressure data to obtain an oral cavity multi-time point prediction result;
Step S42: performing bone tissue morphological change analysis on the oral multi-time-point prediction result to obtain bone tissue morphological change data;
step S43: soft tissue change analysis is carried out on the oral cavity multi-time-point prediction result so as to obtain oral cavity soft tissue change data;
Step S44: performing structural feature point change identification based on the bone tissue morphology change data and the oral cavity soft tissue change data to obtain a change structural feature point;
step S45: analyzing the evolution rate of the part of the feature points of the change structure to obtain a structure evolution rule;
Step S46: and carrying out tissue morphological structure evolution analysis on the oral cavity multi-time-point prediction result according to a structure evolution rule so as to obtain oral cavity morphological evolution prediction data.
The invention predicts the oral cavity state of the oral cavity at different time points by analyzing the mechanical characteristics and the change conditions in the pressure data of the dental implant structure, including the stability of the implant, the change of surrounding bone tissues and the like, so as to obtain the oral cavity multi-time point prediction result, determines the form change conditions of the oral cavity bone tissues at different time points by comparing and analyzing the oral cavity multi-time point prediction result, including the processes of bone absorption, bone formation and the like, so as to provide the oral cavity bone tissue form change data, help to know the interaction and change conditions of the implant and the surrounding bone tissues, determine the change conditions of the oral cavity soft tissues at different time points, including the form of gum, the thickness of soft tissues and the like, help to know the interaction and change conditions of the implant and the surrounding soft tissues, determining the position and shape change of key structural feature points in the oral cavity by analyzing the change condition of bone tissue and soft tissue, such as the change of bone tissue around an implant, the change of the position of gum line and the like, thereby obtaining changed structural feature points, providing quantitative data of the evolution of the oral cavity structure, calculating the rate and trend of the evolution of the oral cavity structure by analyzing the position and the change amplitude of the changed structural feature points, knowing the evolution condition of the oral cavity structure at different time points, including the absorption of bone tissue, the change of soft tissue and the like, thereby obtaining the evolution rule of the oral cavity structure, deducing the shape change of the oral cavity at future time points, including the stability of the implant, the change of the surrounding bone tissue and soft tissue and the like by combining the evolution rule of the oral cavity structure and the oral cavity multipoint prediction result, thereby providing the prediction data of the oral cavity shape evolution, and helps clinical decision-making and treatment planning.
Preferably, the specific steps of step S5 are:
Step S51: calculating the maximum deformation area of the oral morphology evolution prediction data to obtain the maximum deformation area of the oral morphology;
step S52: performing time sequence trend curve fitting on the maximum deformation area of the oral cavity morphology to obtain a morphology evolution time sequence trend curve;
Step S53: performing boundary buffer area analysis on the first dental implant model based on the morphological evolution time sequence trend curve to generate an implant deformation buffer area;
Step S54: and performing stress topological optimization on the implant shape buffer zone to obtain the deformation intelligent buffer zone.
The method and the device determine the area with the most severe morphological change in the oral cavity, namely the maximum deformation area, which relates to bone tissues, soft tissues and the like around the implant, position the parts needing to be focused and processed in the oral cavity by calculating the maximum deformation area, fit a time sequence trend curve of morphological evolution by analyzing the change condition of the maximum deformation area of the oral cavity, reflect the trend of the oral cavity morphology along with time, better know the evolution speed and trend of the oral cavity morphology, provide basis for further analysis and decision, determine the boundary buffer area around the implant, namely the area affected by deformation of the implant by analyzing the relationship between the time sequence trend of the oral cavity morphology and the first dental implant model, improve the stability and success rate of the implant, determine the optimal deformation buffer area by analyzing and optimizing the implant deformation buffer area, provide better support and protect the implant, and help determine the material distribution and structural design in the deformation buffer area, so as to furthest reduce the adverse effects of stress concentration and deformation on the implant, improve the stability of the implant, reduce the stress of the intelligent buffer area and the poor deformation model, and promote the good forming of the implant.
Preferably, the specific steps of step S53 are:
step S531: performing deformation rate matching analysis on the first dental implant model based on the morphological evolution time sequence trend curve to generate dental implant model deformation rate data;
step S532: analyzing the regional stress change rule of the deformation rate data of the dental implant model to obtain the regional stress change rule;
step S533: and analyzing the boundary buffer area based on the area stress change rule to generate an implant deformation buffer area.
According to the method, the deformation degree of the implant model at different time points is determined through the matching analysis of the time sequence trend curve and the dental implant model, the morphological change condition of the implant at different time points is known, a basis is provided for the subsequent steps, the change rule of the forces applied to different areas in the deformation process is determined through the analysis of deformation rate data of the different areas of the dental implant model, the deformation condition of each area in the implant model is known, the areas with larger or uneven stress are identified, the areas needing to be subjected to boundary buffer area analysis are determined, the areas needing special attention and treatment in the implant model, namely the areas needing larger or uneven force are determined through the analysis of the stress change rule of the areas, the areas are considered as potential risk areas of deformation, the boundary areas needing to be buffered in the implant model are determined through the boundary buffer area analysis, so that better support and protection are provided, the adverse effects of the deformation on the implant are reduced, and the generated deformation buffer area is beneficial to the success of the forming and clinical treatment of the dental implant model.
Preferably, the specific steps of step S6 are:
Step S61: performing topological reconstruction on the first dental implant model based on the deformation intelligent buffer zone to construct a second dental implant model;
step S62: performing simulated implantation on the three-dimensional alveolar bone model through the second dental implant model to obtain a holographic alveolar-dental implant model;
Step S63: carrying out dental implant position identification on the holographic tooth socket-dental implant model to obtain embedded position data;
Step S64: carrying out critical tissue pressure distribution quantification on the holographic tooth socket-dental implant model based on the embedded position data to obtain tissue pressure quantification data;
Step S65: and carrying out stress area optimization design on the second dental implant model based on the tissue pressure quantification data so as to construct an optimized dental implant model.
The invention utilizes the data of the deformation intelligent buffer zone to carry out the shape reconstruction on the first dental implant model, so that the shape of the first dental implant model is more suitable for the change of the oral cavity shape, thus obtaining a second dental implant model, the shape of the second dental implant model is more suitable for the actual situation, the suitability and the stability of the model in the oral cavity are improved, the position and the interaction of a dental implant in an alveolar bone are simulated by combining the second dental implant model with a three-dimensional alveolar bone model, the obtained holographic dental implant model more accurately reflects the embedding situation of the dental implant, a more real oral cavity structure model is provided, the specific position of the dental implant in the oral cavity is determined by analyzing the holographic dental implant model, the obtained embedding position data is provided for a clinician to refer, and the dental implant is accurately positioned in the actual operation, ensuring the accuracy and precision of implantation, determining the pressure distribution condition of adjacent tissues around the dental implant by analyzing the holographic tooth socket-dental implant model, obtaining tissue pressure quantification data for evaluating the stress condition of the tissues around the implant, providing reference for clinicians to determine the stability of the implant and the influence degree of the implant on the surrounding tissues, determining the region with larger stress or uneven stress in the dental implant model by analyzing the tissue pressure quantification data, optimally designing the second dental implant model based on the data, adjusting the shape and structure of the second dental implant model to lighten the region with concentrated stress, improving the stability of the dental implant and the adaptability of the alveolar bone, enabling the optimized dental implant model to better share the chewing force, reducing the adverse effect on the surrounding tissues and improving the long-term success rate of the implant, is helpful for the shaping of the dental implant model and the success of clinical treatment.
Drawings
FIG. 1 is a schematic flow chart of steps of a method for forming a dental implant model based on image processing according to the present invention;
FIG. 2 is a detailed implementation step flow diagram of step S1;
FIG. 3 is a detailed implementation step flow diagram of step S2;
Fig. 4 is a detailed implementation step flow diagram of step S3.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a dental implant model forming method based on image processing. The execution main body of the dental implant model forming method based on image processing comprises, but is not limited to, a system carrying: mechanical devices, data processing platforms, cloud server nodes, network uploading devices, etc. may be considered general purpose computing nodes of the present application, including but not limited to: at least one of an audio image management system, an information management system and a cloud data management system.
Referring to fig. 1 to 4, the present invention provides a dental implant model forming method based on image processing, comprising the following steps:
Step S1: acquiring an oral CT image of a patient; carrying out multi-channel convolution treatment on the CT image of the oral cavity of the patient to construct an oral cavity convolution characteristic diagram; performing pixel-level semantic segmentation on the oral convolution feature map to segment the map in an oral area;
Step S2: generating a tooth missing region map based on the oral region segmentation map; carrying out morphological structure analysis on the tooth missing region graph to obtain tooth groove shape structure data; carrying out three-dimensional construction on the tooth socket shape structure data to generate a three-dimensional tooth socket bone model;
Step S3: performing topological form fitting based on the three-dimensional alveolar bone model to construct a first dental implant model; performing pressure distribution calculation around the dental implant on the first dental implant model to generate dental implant structure pressure data;
step S4: carrying out oral cavity multi-time sequence node prediction based on dental implant structure pressure data to obtain an oral cavity multi-time point prediction result; carrying out tissue morphological structure evolution analysis on the oral cavity multi-time-point prediction result to obtain oral cavity morphological evolution prediction data;
Step S5: performing time sequence trend curve fitting on the oral cavity form evolution prediction data to obtain a form evolution time sequence trend curve; performing boundary buffer area analysis on the first dental implant model based on the morphological evolution time sequence trend curve to obtain a deformation intelligent buffer area;
step S6: performing simulated implantation on the first dental implant model based on the deformation intelligent buffer zone to obtain a holographic tooth socket-dental implant model; and carrying out stress area optimization design based on the holographic tooth socket-dental implant model to construct an optimized dental implant model.
The invention processes and segments the oral cavity CT image of the patient to obtain an accurate segmentation map of the oral cavity region, extracts key features in the oral cavity image through multichannel convolution processing to help subsequent analysis and processing, and further classifies each pixel in the oral cavity convolution feature map to enable each pixel to be accurately belonged to the oral cavity region or other regions, thereby obtaining an oral cavity region segmentation map, further analyzing and processing according to the oral cavity region segmentation map to obtain a tooth missing region map, performing morphological structure analysis on the tooth missing region map to obtain morphological structure data of tooth sockets, including shape, size, position and other information, then converting the obtained tooth socket morphological structure data into a three-dimensional tooth socket bone model through a three-dimensional construction technology to facilitate subsequent processing and analysis, performing topological shape fitting by utilizing the three-dimensional tooth socket bone model, performing dental implant surrounding pressure distribution calculation on the first tooth socket bone model according to obtain dental implant structural pressure data, optimizing the oral cavity state of the oral cavity environment, providing a predicted evolution state according to the oral cavity structural development point, and the predicted evolution point, and providing the predicted state of the oral cavity with optimal state, and providing the predicted state of the oral cavity with the predicted state of the oral cavity structure, and optimizing the oral cavity state of the oral cavity structure according to the predicted state of the predicted state, thereby obtaining the predicted state of the oral cavity state, taking into consideration changes of oral morphology, providing a certain fault-tolerant space to adapt to future changes, improving stability and adaptability of the dental implant, utilizing a deformation intelligent buffer zone to carry out simulation implantation on a first dental implant model into the oral model to obtain a complete holographic tooth socket-dental implant model, taking the trend of oral morphology evolution into consideration, providing a buffer zone adapting to future changes, carrying out stress area optimization design based on the holographic tooth socket-dental implant model, determining the stress area in the dental implant model by analyzing the stress distribution in the oral cavity, carrying out optimization design, improving the stability and adaptability of the dental implant, and ensuring long-term success of the dental implant in the oral environment.
In the embodiment of the present invention, referring to fig. 1, a schematic flow chart of steps of a method for forming a dental implant model based on image processing according to the present invention is shown, where in this example, the steps of the method for forming a dental implant model based on image processing include:
Step S1: acquiring an oral CT image of a patient; carrying out multi-channel convolution treatment on the CT image of the oral cavity of the patient to construct an oral cavity convolution characteristic diagram; performing pixel-level semantic segmentation on the oral convolution feature map to segment the map in an oral area;
In this embodiment, a suitable medical imaging device, such as a CT scanner, is used to scan the oral cavity of a patient, obtain three-dimensional CT image data of the oral cavity, take the oral cavity CT image as input, apply a multi-channel Convolutional Neural Network (CNN) to process, preprocess the image, such as resizing, clipping or filling, to adapt to the input requirement of the network, input the preprocessed image into the convolutional network, the convolutional network will extract feature information of the image through a series of convolutional layers, activation functions and pooling layers, obtain an oral cavity convolutional feature map from the last layer or middle layer of the convolutional network, these feature maps are high-dimensional data representations, wherein each channel corresponds to a different feature, predict a label for each pixel, segment the oral cavity region from other regions, and generate an oral cavity region segmentation map.
Step S2: generating a tooth missing region map based on the oral region segmentation map; carrying out morphological structure analysis on the tooth missing region graph to obtain tooth groove shape structure data; carrying out three-dimensional construction on the tooth socket shape structure data to generate a three-dimensional tooth socket bone model;
In this embodiment, a tooth missing region map is generated through pixel level operation, in the oral region segmentation map, the tooth missing region is marked as a positive sample, other regions are marked as a negative sample, the tooth missing region map is processed through morphological operations such as erosion, expansion, open operation or closed operation, and the like, for removing noise, filling cavities, smoothing boundaries, and the like, morphological structure analysis further optimizes the shape and edges of the tooth missing region, and is realized through converting two-dimensional morphological structure data into three-dimensional coordinate points or voxel representations, common methods include curved surface reconstruction, volume rendering or voxelization, and the like, and an alveolar bone model with geometric shapes and topological structures is generated through three-dimensional construction technology.
Step S3: performing topological form fitting based on the three-dimensional alveolar bone model to construct a first dental implant model; performing pressure distribution calculation around the dental implant on the first dental implant model to generate dental implant structure pressure data;
In this embodiment, the design parameters (such as diameter, length, angle, etc.) of the first dental implant are fitted to the three-dimensional alveolar bone model, the topological shape fitting aims at finding the position and direction suitable for the implant in the alveolar bone model to realize the stable implantation of the dental implant, the three-dimensional model of the first dental implant is generated according to the topological shape fitting result, the three-dimensional model is realized by placing a model with proper shape and size in the alveolar bone model, the position and the geometric shape of the first dental implant are represented, the pressure analysis is performed on the first dental implant model, the stress condition of the surrounding tissue of the implant in the chewing or biting process is simulated, and the pressure data of the dental implant structure is calculated and generated according to the simulation result.
Step S4: carrying out oral cavity multi-time sequence node prediction based on dental implant structure pressure data to obtain an oral cavity multi-time point prediction result; carrying out tissue morphological structure evolution analysis on the oral cavity multi-time-point prediction result to obtain oral cavity morphological evolution prediction data;
In this embodiment, the pressure data of the dental implant structure with multiple oral timings is used as input, a time sequence prediction method is applied to perform node prediction of multiple oral timings, the method is implemented by a statistical model, machine learning or deep learning, etc., the prediction result is the state or characteristic of each node (such as teeth, alveolar bone, etc.) in the oral cavity at multiple future time points, according to the prediction result, a prediction model or morphological feature of the oral cavity at different time points is generated, including information of tooth position, alveolar bone density, tissue pressure distribution, etc., for each time point, a corresponding oral model or morphological parameter is generated, and morphological structure evolution analysis is performed by comparing the prediction model or morphological parameters of different time points of the oral cavity, including calculating indexes such as morphological change amount, morphological change rate, etc., so as to understand the dynamic change condition of the oral cavity morphology.
Step S5: performing time sequence trend curve fitting on the oral cavity form evolution prediction data to obtain a form evolution time sequence trend curve; performing boundary buffer area analysis on the first dental implant model based on the morphological evolution time sequence trend curve to obtain a deformation intelligent buffer area;
In this embodiment, the method includes fitting the predicted oral morphology evolution data to a time-series trend curve, fitting the predicted oral morphology evolution data at a known time point, analyzing the evolution trend of the oral morphology according to the time-series trend curve obtained by fitting, including calculating indexes such as slope and change rate of the curve, so as to understand the change condition of the oral morphology, defining a boundary buffer area of the first dental implant model according to the time-series trend curve, adjusting the shape and size of the buffer area according to the change of the time-series trend curve, and defining a dynamic boundary buffer area which is dynamically adjusted according to the slope or change rate of the time-series trend curve.
Step S6: performing simulated implantation on the first dental implant model based on the deformation intelligent buffer zone to obtain a holographic tooth socket-dental implant model; and carrying out stress area optimization design based on the holographic tooth socket-dental implant model to construct an optimized dental implant model.
In this embodiment, the first dental implant model is registered with the oral cavity model, the dental implant model is placed at a corresponding position in the oral cavity model, numerical simulation is performed, material characteristics, geometric shapes and boundary conditions of teeth, alveolar bones and surrounding tissues are considered, boundary information of a deformation intelligent buffer area is used for limiting the deformation range of the dental implant in the simulation process, stress distribution conditions in the holographic dental implant model are calculated through simulation results and are realized through finite element analysis and other methods, stress analysis reveals stress concentration areas and stress conditions of the dental implant and surrounding tissues, optimal design of the stress areas is performed according to stress analysis results, and the shape, structure or material distribution of the dental implant is adjusted, so that the stress concentration is reduced and the stability of the dental implant is improved.
In this embodiment, referring to fig. 2, a detailed implementation step flow chart of the step S1 is shown, and in this embodiment, the detailed implementation step of the step S1 includes:
step S11: acquiring an oral CT image of a patient;
step S12: carrying out multi-channel convolution processing on the CT image of the oral cavity of the patient so as to extract cross-band characteristic data;
Step S13: constructing an oral convolution feature map based on the cross-band feature data;
step S14: carrying out branch sampling treatment on the oral cavity convolution characteristic map to obtain a space detail convolution map;
Step S15: and carrying out pixel-level semantic segmentation on the space detail convolution map to segment the map of the oral cavity region.
In this embodiment, three-dimensional image data of a patient's oral cavity is obtained through CT scanning, a Convolutional Neural Network (CNN) is used, a plurality of convolutional kernels are applied to the oral cavity CT image to perform convolutional operation, each convolutional kernel extracts specific cross-band features, a plurality of channels of feature images are obtained through the convolutional operation, feature data obtained through the multi-channel convolutional processing are integrated to construct an oral cavity convolutional feature image, the oral cavity convolutional feature image is realized through combination, superposition or other operation of the feature images of each channel, the oral cavity convolutional feature image is subjected to branch sampling operation to extract space detail information, the filter, pooling operation or other sampling technology is used to realize the operation, the sampling operation helps focus on local areas of the oral cavity image, and more detail information is extracted, the space detail convolutional image is obtained through branch sampling processing, wherein the local detail information of the oral cavity image is contained, pixel-level classification is performed on the space detail image by using a pixel-level semantic segmentation algorithm, each pixel of the oral cavity image is assigned to a specific category, such as teeth, gingiva, bones and the like, and the oral cavity area division image is obtained through pixel-level semantic segmentation, wherein each pixel is marked as a corresponding category.
In this embodiment, the specific steps of step S14 are as follows:
Step S141: performing multi-layer downsampling on the oral cavity convolution feature map to obtain global large-granularity feature data;
step S142: performing multi-scale up-sampling on the oral cavity convolution feature map so as to obtain local detail feature data;
step S143: residual connection processing is carried out on the global large-granularity characteristic data and the local detail characteristic data so as to generate missing detail characteristic data;
Step S144: based on the missing detail feature data, a space detail convolution graph is obtained.
In this embodiment, when downsampling is performed, the feature map is divided into non-overlapping areas, the pixel value in each area is obtained by pooling operation, so that the size of the feature map is reduced, global large-granularity feature data is extracted, when upsampling is performed, each pixel value in the feature map is copied to a target position according to the size which is amplified as required, other pixel values are filled by interpolation or convolution and other methods, the size of the feature map is increased, local detail feature data is extracted, residual connection operation is performed on the global large-granularity feature data and the local detail feature data, the corresponding pixel values of the two feature maps are added, residual connection helps to transfer missing detail information, the feature expression capability is improved, the generated missing detail feature data is utilized, convolution operation, filtering operation and the like are further performed, and a space detail convolution map is obtained through processing based on the missing detail feature data, wherein the richer detail information of an oral cavity image is contained.
In this embodiment, referring to fig. 3, a detailed implementation step flow chart of the step S2 is shown, and in this embodiment, the detailed implementation step of the step S2 includes:
step S21: carrying out fine grain structure identification on the oral area segmentation map to obtain a tooth missing area map;
Step S22: extracting the tooth socket profile of the tooth missing region graph to obtain tooth socket region profile data;
Step S23: carrying out morphological structure analysis on the outline data of the tooth socket area to obtain tooth socket shape structure data;
step S24: performing tooth topology gap calculation on the tooth missing region map to obtain peripheral tooth gap range data;
Step S25: and carrying out three-dimensional construction on the tooth socket structural data based on the peripheral tooth gap range data so as to generate a three-dimensional tooth socket bone model.
In this embodiment, on the basis of the segmentation map of the oral cavity region, fine-grained structure recognition is performed to identify the missing region of the tooth, a deep learning method is used, such as Convolutional Neural Network (CNN), classification or pixel-level prediction is performed on the segmentation map, the missing tooth region is marked, edge detection or contour extraction algorithm is performed on the missing region map of the tooth, the contour of the tooth socket region is extracted, gradient-based algorithm (such as Sobel operator or Canny edge detection) or region boundary-based algorithm (such as boundary tracking algorithm) is used, morphological analysis is performed on the contour data of the tooth socket region, morphological operations (such as calculating the length, area, perimeter and the like of the contour are performed, morphological operations (such as erosion, expansion, open operation and closed operation) are applied to further improve the contour shape, remove noise or fill up cavities, analysis is performed on the missing region map of the tooth, topology gaps between the tooth are calculated, the distance between the missing region and the surrounding tooth are measured, the distance between the missing region and the boundary is calculated, the distance between the surrounding tooth body and the boundary are used, the gap range and distribution are calculated by using the technologies such as distance conversion and boundary detection, three-dimensional tooth socket gap range data and tooth socket state structure data are used, three-dimensional reconstruction algorithm is performed, the three-dimensional spline state data is used, the three-dimensional interpolation model is generated, and the three-dimensional interpolation model is generated by using the three-dimensional interpolation algorithm (computer-dimensional interpolation model or computer-aided graph model) or three-dimensional interpolation model.
In this embodiment, as described with reference to fig. 4, a detailed implementation step flow diagram of the step S3 is shown, and in this embodiment, the detailed implementation step of the step S3 includes:
Step S31: performing topological form fitting based on the three-dimensional alveolar bone model to construct a first dental implant model;
Step S32: performing elastic deformation dynamics constraint analysis on the first dental implant model to obtain elastic deformation dynamics constraint data;
Step S33: performing chewing wear simulation on the elastic deformation dynamics constraint data to obtain wear simulation data;
step S34: and performing pressure distribution calculation around the dental implant based on the wear simulation data to generate dental implant structure pressure data.
In this embodiment, the shape of the first dental implant is fitted to the three-dimensional alveolar bone model to obtain the position and posture of the first dental implant, the elastic deformation dynamics constraint analysis is performed on the first dental implant model to simulate the force and pressure applied to the implant during chewing, the combination of the implant model and the material characteristics is performed using Finite Element Analysis (FEA) or other simulation methods, the contact and abrasion between teeth during chewing is simulated based on the elastic deformation dynamics constraint data in consideration of the elastic behavior of tissues and the chewing force, the simulation methods such as the contact force analysis and the friction model are used to combine the chewing motion data and the tooth surface characteristics to simulate the contact, sliding and abrasion process between teeth, the pressure distribution around the dental implant is calculated according to the abrasion simulation data, and the pressure distribution around the dental implant is calculated by combining the abrasion simulation data with the pressure transmission model in consideration of the conduction, distribution and influence of the chewing force.
In this embodiment, step S4 includes the following steps:
step S41: carrying out oral cavity multi-time sequence node prediction based on dental implant structure pressure data to obtain an oral cavity multi-time point prediction result;
Step S42: performing bone tissue morphological change analysis on the oral multi-time-point prediction result to obtain bone tissue morphological change data;
step S43: soft tissue change analysis is carried out on the oral cavity multi-time-point prediction result so as to obtain oral cavity soft tissue change data;
Step S44: performing structural feature point change identification based on the bone tissue morphology change data and the oral cavity soft tissue change data to obtain a change structural feature point;
step S45: analyzing the evolution rate of the part of the feature points of the change structure to obtain a structure evolution rule;
Step S46: and carrying out tissue morphological structure evolution analysis on the oral cavity multi-time-point prediction result according to a structure evolution rule so as to obtain oral cavity morphological evolution prediction data.
In this embodiment, oral cavity multi-time-series node prediction is performed by using oral cavity model and dental implant structure pressure data, a time series analysis method such as regression analysis, machine learning or deep learning model is used to establish an association relationship between dental implant structure pressure data and time nodes, so as to predict the state and change of oral cavity at different time points, bone tissue morphology change analysis is performed on the oral cavity multi-time-point prediction result to identify and quantify bone tissue morphology change, an oral cavity model at different time points is compared by using image processing and analysis methods such as image registration, segmentation and morphology analysis, bone tissue morphology change is detected and measured, soft tissue change analysis is performed on the oral cavity multi-time-point prediction result to identify and quantify oral cavity soft tissue change, image processing and analysis methods such as image registration, segmentation and morphology analysis are used, comparing the oral cavity models at different time points, detecting and measuring the morphological changes of the soft tissues, combining bone tissue morphological change data and oral cavity soft tissue change data, identifying the changes of structural feature points in the oral cavity models, detecting and quantifying the changes of the positions, the shapes or other attributes of the structural feature points by comparing the oral cavity models at different time points, analyzing the evolution rate of the parts of the structural feature points to determine the evolution rule of the oral cavity structure, comparing the changes of the positions and the attributes of the feature points at different time points by using a statistical analysis and pattern identification method, calculating the rate and trend among the feature points to reveal the evolution rule of the oral cavity structure, carrying out tissue morphological structure evolution analysis on the oral cavity multi-time point prediction result based on the structure evolution rule, using interpolation, deformation models or other methods according to the initial state and the structure evolution rule of the oral cavity, predicting the morphological evolution of the oral cavity at a future time point.
In this embodiment, the specific steps of step S5 are as follows:
Step S51: calculating the maximum deformation area of the oral morphology evolution prediction data to obtain the maximum deformation area of the oral morphology;
step S52: performing time sequence trend curve fitting on the maximum deformation area of the oral cavity morphology to obtain a morphology evolution time sequence trend curve;
Step S53: performing boundary buffer area analysis on the first dental implant model based on the morphological evolution time sequence trend curve to generate an implant deformation buffer area;
Step S54: and performing stress topological optimization on the implant shape buffer zone to obtain the deformation intelligent buffer zone.
In this embodiment, analysis is performed on predicted data of oral morphology evolution, a region where the maximum change occurs in the process of morphology evolution is found, morphology analysis, point cloud comparison or other image processing methods are used, oral models on different time points are compared, the degree of morphology change is calculated, the maximum deformation region is determined, time sequence analysis is performed on the morphology evolution of the maximum deformation region of the oral morphology, a time sequence trend curve is fitted, regression analysis, curve fitting or other time sequence analysis methods are used for modeling the morphology change of the maximum deformation region at different time points, a time sequence trend curve of morphology evolution is generated, boundary buffer region analysis is performed on the first dental implant model by using the time sequence trend curve of morphology evolution, the boundary position of the implant model is determined according to the time sequence trend curve of morphology evolution to generate an implant deformation buffer region, stress topology optimization is performed on the implant deformation buffer region, and the structure in the implant deformation buffer region is optimized by using finite element analysis or other structure optimization methods, so that the stress resistance and stability of the implant deformation buffer region are improved, and thus an intelligent buffer region is obtained.
In this embodiment, the specific steps of step S53 are as follows:
step S531: performing deformation rate matching analysis on the first dental implant model based on the morphological evolution time sequence trend curve to generate dental implant model deformation rate data;
step S532: analyzing the regional stress change rule of the deformation rate data of the dental implant model to obtain the regional stress change rule;
Step S533: performing boundary buffer area analysis based on the area stress change rule to generate an implant deformation buffer area;
Step S54: and performing stress topological optimization on the implant shape buffer zone to obtain the deformation intelligent buffer zone.
In this embodiment, deformation rate matching analysis is performed on the first dental implant model by using a morphological evolution time sequence trend curve, deformation rate (i.e., degree of morphological change) of each point is calculated by comparing morphological differences of the model at different time points to generate deformation rate data of the dental implant model, deformation rate data of the dental implant model are analyzed to find rules of stress changes in different areas, areas of significant stress changes are detected by comparing deformation rate data at different time points, change rules of the areas are determined, boundary buffer area analysis is performed on the first dental implant model by using the area stress change rules, boundary positions of the implant model are determined according to the stress change rules to generate an implant deformation buffer area, stress topology optimization is performed on the implant deformation buffer area, and structures in the buffer area are optimized by using finite element analysis or other structure optimization methods to improve stress resistance and stability of the implant deformation intelligent buffer area.
In this embodiment, the specific steps of step S6 are as follows:
Step S61: performing topological reconstruction on the first dental implant model based on the deformation intelligent buffer zone to construct a second dental implant model;
step S62: performing simulated implantation on the three-dimensional alveolar bone model through the second dental implant model to obtain a holographic alveolar-dental implant model;
Step S63: carrying out dental implant position identification on the holographic tooth socket-dental implant model to obtain embedded position data;
Step S64: carrying out critical tissue pressure distribution quantification on the holographic tooth socket-dental implant model based on the embedded position data to obtain tissue pressure quantification data;
Step S65: and carrying out stress area optimization design on the second dental implant model based on the tissue pressure quantification data so as to construct an optimized dental implant model.
In this embodiment, the first dental implant model is topologically reconstructed by using the information of the deformation intelligent buffer, by changing the shape, structure or topological connection of the model, by introducing the shape and structural features of the deformation intelligent buffer into the first dental implant model, generating a second dental implant model with better stress resistance and stability, performing simulated implantation on the second dental implant model and the three-dimensional alveolar bone model, by placing the second dental implant model in a proper position in the alveolar bone model, generating a holographic dental socket-dental implant model which represents the relationship between the dental implant and the alveolar bone and contains embedded position information, by analyzing and processing the holographic dental socket-dental implant model, identifying the position of the dental implant, the method is realized by calculating the relative position and geometric characteristics of the dental implant and the alveolar bone, acquiring data about the accurate position of the dental implant in the oral cavity by identifying the embedding position, quantifying the pressure distribution of the critical tissue of the holographic tooth socket-dental implant model by utilizing the embedding position data, realizing the pressure distribution condition of tissue around the dental implant by calculating the pressure distribution condition of tissue around the dental implant, obtaining detailed data about the stress condition of the tissue around the dental implant by quantifying the tissue pressure, optimally designing the stress area of the second dental implant model by utilizing the tissue pressure quantification data, realizing the optimization design by adjusting the shape, the size or the material of the dental implant, enabling the stress distribution of the dental implant in the oral cavity to be more uniform by optimizing the design, reducing the potential stress concentration area and improving the stability and the long-term success rate of the dental implant.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
The foregoing is merely a specific embodiment of the invention to enable a person skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. The method for forming the dental implant model based on image processing is characterized by comprising the following steps of:
Step S1: acquiring an oral CT image of a patient; carrying out multi-channel convolution treatment on the CT image of the oral cavity of the patient to construct an oral cavity convolution characteristic diagram; performing pixel-level semantic segmentation on the oral convolution feature map to segment the map in an oral area;
Step S2: generating a tooth missing region map based on the oral region segmentation map; carrying out morphological structure analysis on the tooth missing region graph to obtain tooth groove shape structure data; carrying out three-dimensional construction on the tooth socket shape structure data to generate a three-dimensional tooth socket bone model;
Step S3: performing topological form fitting based on the three-dimensional alveolar bone model to construct a first dental implant model; performing pressure distribution calculation around the dental implant on the first dental implant model to generate dental implant structure pressure data;
step S4: carrying out oral cavity multi-time sequence node prediction based on dental implant structure pressure data to obtain an oral cavity multi-time point prediction result; carrying out tissue morphological structure evolution analysis on the oral cavity multi-time-point prediction result to obtain oral cavity morphological evolution prediction data;
Step S5: performing time sequence trend curve fitting on the oral cavity form evolution prediction data to obtain a form evolution time sequence trend curve; performing boundary buffer area analysis on the first dental implant model based on the morphological evolution time sequence trend curve to obtain a deformation intelligent buffer area;
step S6: performing simulated implantation on the first dental implant model based on the deformation intelligent buffer zone to obtain a holographic tooth socket-dental implant model; and carrying out stress area optimization design based on the holographic tooth socket-dental implant model to construct an optimized dental implant model.
2. The method for forming a dental implant model based on image processing according to claim 1, wherein the specific steps of step S1 are:
step S11: acquiring an oral CT image of a patient;
step S12: carrying out multi-channel convolution processing on the CT image of the oral cavity of the patient so as to extract cross-band characteristic data;
Step S13: constructing an oral convolution feature map based on the cross-band feature data;
step S14: carrying out branch sampling treatment on the oral cavity convolution characteristic map to obtain a space detail convolution map;
Step S15: and carrying out pixel-level semantic segmentation on the space detail convolution map to segment the map of the oral cavity region.
3. The image processing-based dental implant model forming method according to claim 2, wherein the specific steps of step S14 are:
Step S141: performing multi-layer downsampling on the oral cavity convolution feature map to obtain global large-granularity feature data;
step S142: performing multi-scale up-sampling on the oral cavity convolution feature map so as to obtain local detail feature data;
step S143: residual connection processing is carried out on the global large-granularity characteristic data and the local detail characteristic data so as to generate missing detail characteristic data;
Step S144: based on the missing detail feature data, a space detail convolution graph is obtained.
4. The method for forming a dental implant model based on image processing according to claim 1, wherein the specific steps of step S2 are:
step S21: carrying out fine grain structure identification on the oral area segmentation map to obtain a tooth missing area map;
Step S22: extracting the tooth socket profile of the tooth missing region graph to obtain tooth socket region profile data;
Step S23: carrying out morphological structure analysis on the outline data of the tooth socket area to obtain tooth socket shape structure data;
step S24: performing tooth topology gap calculation on the tooth missing region map to obtain peripheral tooth gap range data;
Step S25: and carrying out three-dimensional construction on the tooth socket structural data based on the peripheral tooth gap range data so as to generate a three-dimensional tooth socket bone model.
5. The method for forming a dental implant model based on image processing according to claim 1, wherein the specific step of step S3 is:
Step S31: performing topological form fitting based on the three-dimensional alveolar bone model to construct a first dental implant model;
Step S32: performing elastic deformation dynamics constraint analysis on the first dental implant model to obtain elastic deformation dynamics constraint data;
Step S33: performing chewing wear simulation on the elastic deformation dynamics constraint data to obtain wear simulation data;
step S34: and performing pressure distribution calculation around the dental implant based on the wear simulation data to generate dental implant structure pressure data.
6. The method for forming a dental implant model based on image processing according to claim 1, wherein the specific step of step S4 is:
step S41: carrying out oral cavity multi-time sequence node prediction based on dental implant structure pressure data to obtain an oral cavity multi-time point prediction result;
Step S42: performing bone tissue morphological change analysis on the oral multi-time-point prediction result to obtain bone tissue morphological change data;
step S43: soft tissue change analysis is carried out on the oral cavity multi-time-point prediction result so as to obtain oral cavity soft tissue change data;
Step S44: performing structural feature point change identification based on the bone tissue morphology change data and the oral cavity soft tissue change data to obtain a change structural feature point;
step S45: analyzing the evolution rate of the part of the feature points of the change structure to obtain a structure evolution rule;
Step S46: and carrying out tissue morphological structure evolution analysis on the oral cavity multi-time-point prediction result according to a structure evolution rule so as to obtain oral cavity morphological evolution prediction data.
7. The method for forming a dental implant model based on image processing according to claim 1, wherein the specific steps of step S5 are:
Step S51: calculating the maximum deformation area of the oral morphology evolution prediction data to obtain the maximum deformation area of the oral morphology;
step S52: performing time sequence trend curve fitting on the maximum deformation area of the oral cavity morphology to obtain a morphology evolution time sequence trend curve;
Step S53: performing boundary buffer area analysis on the first dental implant model based on the morphological evolution time sequence trend curve to generate an implant deformation buffer area;
Step S54: and performing stress topological optimization on the implant shape buffer zone to obtain the deformation intelligent buffer zone.
8. The image processing-based dental implant model forming method according to claim 7, wherein the specific steps of step S53 are:
step S531: performing deformation rate matching analysis on the first dental implant model based on the morphological evolution time sequence trend curve to generate dental implant model deformation rate data;
step S532: analyzing the regional stress change rule of the deformation rate data of the dental implant model to obtain the regional stress change rule;
step S533: and analyzing the boundary buffer area based on the area stress change rule to generate an implant deformation buffer area.
9. The method for forming a dental implant model based on image processing according to claim 1, wherein the specific step of step S6 is:
Step S61: performing topological reconstruction on the first dental implant model based on the deformation intelligent buffer zone to construct a second dental implant model;
step S62: performing simulated implantation on the three-dimensional alveolar bone model through the second dental implant model to obtain a holographic alveolar-dental implant model;
Step S63: carrying out dental implant position identification on the holographic tooth socket-dental implant model to obtain embedded position data;
Step S64: carrying out critical tissue pressure distribution quantification on the holographic tooth socket-dental implant model based on the embedded position data to obtain tissue pressure quantification data;
Step S65: and carrying out stress area optimization design on the second dental implant model based on the tissue pressure quantification data so as to construct an optimized dental implant model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410436561.XA CN118021474B (en) | 2024-04-11 | 2024-04-11 | Dental implant model forming method based on image processing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410436561.XA CN118021474B (en) | 2024-04-11 | 2024-04-11 | Dental implant model forming method based on image processing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118021474A true CN118021474A (en) | 2024-05-14 |
| CN118021474B CN118021474B (en) | 2024-07-05 |
Family
ID=91000783
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410436561.XA Active CN118021474B (en) | 2024-04-11 | 2024-04-11 | Dental implant model forming method based on image processing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118021474B (en) |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100105009A1 (en) * | 2008-10-29 | 2010-04-29 | Inpronto Inc. | System, Method And Apparatus For Tooth Implants |
| WO2013106463A2 (en) * | 2012-01-09 | 2013-07-18 | Old Dominion University Research Foundation | Method and device for natural root form dental implants |
| CN107184285A (en) * | 2017-06-15 | 2017-09-22 | 上海贝齿信息科技有限公司 | The dentistry implant implantation of design by chair can be positioned, guiding device step by step for hole |
| CN109446738A (en) * | 2018-12-20 | 2019-03-08 | 浙江工业大学 | Bone face anchoring type dental implant design method and planting body with topological optimization structure |
| CN209182826U (en) * | 2018-12-20 | 2019-07-30 | 浙江工业大学 | Bone face anchoring type dental implant with topological optimization structure |
| KR20190142080A (en) * | 2018-06-15 | 2019-12-26 | 오스템임플란트 주식회사 | Dental implant planning method, apparatus, and recording medium thereof |
| CN111358585A (en) * | 2020-03-18 | 2020-07-03 | 山东大学 | CT image-based porous implant manufacturing method and system |
| CN111388125A (en) * | 2020-03-05 | 2020-07-10 | 深圳先进技术研究院 | Method and device for calculating tooth movement amount before and after orthodontic treatment |
| CN112120810A (en) * | 2020-09-29 | 2020-12-25 | 深圳市深图医学影像设备有限公司 | Three-dimensional data generation method of tooth orthodontic concealed appliance |
| CN113711271A (en) * | 2019-03-15 | 2021-11-26 | 豪夫迈·罗氏有限公司 | Deep convolutional neural network for tumor segmentation by positron emission tomography |
| CN115954088A (en) * | 2022-10-19 | 2023-04-11 | 浙江大学 | Method for obtaining root shield dental implant optimal model based on reverse three-dimensional finite element analysis |
| CN116052890A (en) * | 2022-11-18 | 2023-05-02 | 江苏创英医疗器械有限公司 | Tooth implant three-dimensional scanning modeling system and method based on Internet of things |
| CN116663086A (en) * | 2023-07-28 | 2023-08-29 | 南昌东森牙科器材有限公司 | Method for reversely constructing implant based on biomechanical analysis grid cell mapping |
-
2024
- 2024-04-11 CN CN202410436561.XA patent/CN118021474B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100105009A1 (en) * | 2008-10-29 | 2010-04-29 | Inpronto Inc. | System, Method And Apparatus For Tooth Implants |
| WO2013106463A2 (en) * | 2012-01-09 | 2013-07-18 | Old Dominion University Research Foundation | Method and device for natural root form dental implants |
| CN107184285A (en) * | 2017-06-15 | 2017-09-22 | 上海贝齿信息科技有限公司 | The dentistry implant implantation of design by chair can be positioned, guiding device step by step for hole |
| KR20190142080A (en) * | 2018-06-15 | 2019-12-26 | 오스템임플란트 주식회사 | Dental implant planning method, apparatus, and recording medium thereof |
| CN109446738A (en) * | 2018-12-20 | 2019-03-08 | 浙江工业大学 | Bone face anchoring type dental implant design method and planting body with topological optimization structure |
| CN209182826U (en) * | 2018-12-20 | 2019-07-30 | 浙江工业大学 | Bone face anchoring type dental implant with topological optimization structure |
| CN113711271A (en) * | 2019-03-15 | 2021-11-26 | 豪夫迈·罗氏有限公司 | Deep convolutional neural network for tumor segmentation by positron emission tomography |
| CN111388125A (en) * | 2020-03-05 | 2020-07-10 | 深圳先进技术研究院 | Method and device for calculating tooth movement amount before and after orthodontic treatment |
| CN111358585A (en) * | 2020-03-18 | 2020-07-03 | 山东大学 | CT image-based porous implant manufacturing method and system |
| CN112120810A (en) * | 2020-09-29 | 2020-12-25 | 深圳市深图医学影像设备有限公司 | Three-dimensional data generation method of tooth orthodontic concealed appliance |
| CN115954088A (en) * | 2022-10-19 | 2023-04-11 | 浙江大学 | Method for obtaining root shield dental implant optimal model based on reverse three-dimensional finite element analysis |
| CN116052890A (en) * | 2022-11-18 | 2023-05-02 | 江苏创英医疗器械有限公司 | Tooth implant three-dimensional scanning modeling system and method based on Internet of things |
| CN116663086A (en) * | 2023-07-28 | 2023-08-29 | 南昌东森牙科器材有限公司 | Method for reversely constructing implant based on biomechanical analysis grid cell mapping |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118021474B (en) | 2024-07-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Silva et al. | Automatic segmenting teeth in X-ray images: Trends, a novel data set, benchmarking and future perspectives | |
| Zanjani et al. | Mask-MCNet: Tooth instance segmentation in 3D point clouds of intra-oral scans | |
| Hwang et al. | Learning beyond human expertise with generative models for dental restorations | |
| CN110717905B (en) | Brain image detection method, computer device, and storage medium | |
| CN113223010B (en) | Method and system for multi-tissue full-automatic segmentation of oral cavity image | |
| Tian et al. | Efficient computer-aided design of dental inlay restoration: a deep adversarial framework | |
| CN112991273B (en) | Orthodontic feature automatic detection method and system of three-dimensional tooth model | |
| CN112785609B (en) | CBCT tooth segmentation method based on deep learning | |
| CN114638852A (en) | Jaw bone and soft tissue identification and reconstruction method, device and medium based on CBCT image | |
| CN114998301B (en) | Vertebral body sub-region segmentation method and device and storage medium | |
| CN112085740A (en) | Tooth fast segmentation method based on three-dimensional tooth jaw model | |
| CN110634132A (en) | Method for automatically generating tuberculosis quantitative diagnosis report based on deep learning 3D CT image | |
| CN115409811A (en) | Tooth model reconstruction method, device, equipment and medium based on curvature enhancement | |
| Türker et al. | Fabrication of Customized dental guide by stereolithography method and evaluation of dimensional accuracy with artificial neural networks | |
| CN114757908A (en) | Image processing method, device and equipment based on CT image and storage medium | |
| CN118021474B (en) | Dental implant model forming method based on image processing | |
| CN113838048A (en) | Cruciate ligament preoperative insertion center positioning and ligament length calculating method | |
| Broll et al. | Generative deep learning approaches for the design of dental restorations: A narrative review | |
| KR102476888B1 (en) | Artificial diagnostic data processing apparatus and its method in digital pathology images | |
| Nassar | A prototype automatic dental identification system (ADIS) | |
| CN115761226A (en) | Oral cavity image segmentation identification method and device, electronic equipment and storage medium | |
| CN115439409A (en) | Tooth type identification method and device | |
| CN115409812A (en) | CT image automatic classification method based on fusion time attention mechanism | |
| Dhar et al. | Automatic tracing of mandibular canal pathways using deep learning | |
| CN112967295A (en) | Image processing method and system based on residual error network and attention mechanism |
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 |