281 results about "Cbct image" patented technology

The present invention relates to the technical field of stomatology. Provided is an auxiliary dental implant generation method based on a diffusion model. The method in the present invention comprises: acquiring oral CBCT image data of historical patients, preprocessing the oral CBCT image data of the historical patients to obtain a CBCT image dataset, using the CBCT image dataset to train a multi-task segmentation network, and using the segmentation network to obtain an intraoral tissue segmentation result; using the intraoral tissue segmentation result to train detection networks from the three dimensions of a cross-sectional plane, a coronal plane and a sagittal plane, respectively; using the detection networks to obtain detection results in the three directions of the cross-sectional plane, the coronal plane and the sagittal plane; fusing the detection results in the three directions of the cross-sectional plane, the coronal plane and the sagittal plane, and using a majority voting algorithm to construct a three-dimensional bounding box, so as to acquire an edentulous area; and using the intraoral segmentation result and the edentulous area as prompt information to guide, by means of an iterative process, a network to generate a post-implantation effect. The implantation effect obtained by the present invention is highly accurate, thereby providing a more precise auxiliary tool for stomatology.

The invention discloses a three-dimensional stomatognathic model reconstruction system based on multi-modal data fusion. The system comprises a multi-modal data input unit, a template deformation reconstruction unit, a registration fusion unit, a multi-source data integration unit and an output unit. Through fusion processing of a CBCT image, an oral cavity vision measurement model and facial scanning data, a body deformation algorithm is adopted to couple biomechanical characteristics to realize craniojaw template deformation, and a non-rigid ICP algorithm is combined for dynamic regulation and control to realize facial template adaptation. A deep neural network is innovatively constructed to segment CBCT gingival data, the CBCT gingival data is fused with an oral cavity vision measurement model, and high-precision tooth reconstruction is realized by applying a differential geometry multi-scale curvature field segmentation and adversarial edge optimization technology. Through a composite registration strategy combining adaptive rigid registration and non-rigid registration, an occlusal plane constraint mechanism and an orbital curvature extreme point matching algorithm are innovatively introduced, finally, multi-source data high-precision registration fusion is realized, and a three-dimensional oral-jaw system model with anatomical structure integrity and clinical precision can be generated.

The invention discloses a sparse finite angle CBCT reconstruction method and system based on residual diffusion and a storage medium, and the method comprises the steps: carrying out the CBCT sparse finite angle scanning of a to-be-detected target, and obtaining sparse projection data; fDK reconstruction is carried out on the sparse projection data to obtain an initial CBCT image; generating a first optimized CBCT image from the initial CBCT image through an image pre-training network; through the first optimized CBCT image and the sparse projection data, using the trained residual diffusion model to determine a residual image of the to-be-detected target; summing the first optimized CBCT image of the to-be-detected target and the residual image of the to-be-detected target to obtain a second optimized CBCT image of the to-be-detected target, and the second optimized CBCT image is a final CBCT reconstruction image. According to the method, the problems of stripe artifacts and low-contrast tissue annihilation under limited angle scanning are solved, the large-view CBCT reconstruction resolution is improved, and the radiation dose is reduced.

The invention relates to the technical field of oral medical treatment, in particular to an oral disease analysis method and system based on AI intelligent recognition. Comprising the following steps that oral cavity three-dimensional surface data are obtained through an oral scanning module integrated with a dental chair, and the oral scanning module adopts a structured light coding and multi-view fusion algorithm; an oral soft tissue high-definition image is collected through an endoscope module, and an AI analysis unit is arranged in an endoscope to recognize mucous membrane lesion features in real time; calling CBCT image data of the patient, and aligning a three-dimensional image with oral scanning data through a spatial registration algorithm; the mouth scanning data, the endoscope image and the CBCT data are subjected to multi-modal fusion by adopting a dynamic weight fusion mechanism. According to the scheme, the problem of accurate registration of cross-modal data, the lack of a dynamic quality evaluation mechanism of heterogeneous data and the real-time collaboration bottleneck of dispersed equipment can be solved.

The invention discloses a tooth segmentation method of a CBCT (Cone Beam Computed Tomography) image in the technical field of medical image segmentation. The method comprises the following steps: S1, sequentially carrying out standardization and data enhancement processing on a CBCT image data set; s2, inputting the CBCT image preprocessed in the S1 into an encoder part of a network structure, and completing step-by-step extraction of image features to obtain multi-scale features; s3, further fusing the multi-scale feature maps generated by each downsampling layer of the encoder network structure to obtain richer and more effective feature expressions; and S4, performing step-by-step spatial resolution recovery on the encoder features, and finally realizing fine segmentation of the tooth image, the tooth segmentation method of the CBCT image solves the problem that the teeth and surrounding tissue boundary details are fuzzy and the teeth are not clear due to the influence of metal artifacts in CBCT segmentation.

The invention discloses a mandibular neural tube automatic segmentation method based on multi-view guidance, and relates to the technical field of oral medical treatment. Comprising the following steps: collecting original oral cavity CBCT image data, and performing preprocessing; sending the preprocessed image data into a mandible segmentation network, and segmenting a mandible region containing a mandible neural tube; a contrast enhancement technology is adopted for the segmented mandible area; projecting the processed mandible image along three orthogonal tangent planes of a coronal plane, a sagittal plane and a cross section; performing key point detection on the mandibular neural tubes on the three projection planes by using the trained key point detection network to obtain shape information of the mandibular neural tubes; and the features are combined with texture direction features to be input into a three-dimensional CBCT mandibular neural tube segmentation network, and accurate 3D segmentation is carried out. Two-dimensional missing space information is avoided, and mandibular neural tube segmentation is more complete and accurate.

The embodiment of the invention discloses a root canal feature form extraction method, device and equipment based on CBCT data and a medium, and belongs to the technical field of stomatology and medical image.The method comprises the steps that filtering denoising and dental pulp segmentation processing are carried out on a CBCT image, and surface drawing three-dimensional reconstruction is carried out on a tooth root canal to generate a three-dimensional model; based on regular point cloud data in the three-dimensional model, adopting a point-surface method to realize curved surface reconstruction, and generating a root canal entity model; and performing coordinate alignment processing on the root canal solid model in a three-dimensional space, and measuring the length of the root canal, the direction of a root canal orifice, the curvature of the root canal and the depth of dental pulp by fitting the approximate center line of the root canal. According to the method, the corresponding three-dimensional model is quickly reconstructed based on the CBCT cross-sectional image obtained by scanning the teeth of the patient, and related data such as the dental pulp depth and the size of the root canal orifice are acquired from the three-dimensional model of the teeth so as to assist the oral surgery.

The invention discloses a CBCT tooth segmentation method and system based on an anatomical perception cascade network, and the method comprises the steps: a first stage, carrying out the simplified dichotomy segmentation based on an original CBCT image and a coarse segmentation network taking 3D U-Net as a trunk, outputting maxillary and mandibular tooth probability graphs, and taking the maxillary and mandibular tooth probability graphs as prior information to guide the generation of an SDM; in the second stage, the original CBCT image and the calibrated maxillary tooth probability graph and the calibrated mandibular tooth probability graph are spliced together, a formed multi-channel input tensor is input into a fine segmentation network, the fine segmentation network takes Residual U-Net as a trunk, and an improved AGBR module and an improved SDMAA module are integrated in an encoder-decoder architecture of the fine segmentation network; and the decoder fuses all refined and re-calibrated feature maps, and upsamples and reconstructs 42 types of instance segmentation results with correct topology and clear boundaries. According to the method, the problem that in the prior art, when the inherent and local boundary fuzzy defect in CBCT is overcome, an effective pertinence mechanism is lacked is solved, and precise and robust 42-class instance segmentation can be achieved.

The invention relates to an oral and maxillofacial implant repair path generation system based on a CBCT image, in particular to the field of computer aided design of surgical operations, and the scheme comprises the following steps: constructing a high-quality training data set through multi-source data aggregation and intelligent labeling enhancement; training by using the mixed model to generate a high-precision jaw segmentation model; the segmentation parameters are dynamically adjusted in combination with real-time feedback in preoperative planning, and a personalized optimization result is output; the model is continuously updated and the data strategy is optimized by means of a closed-loop feedback mechanism, so that the planning accuracy, the self-adaptability and the clinical efficiency of oral maxillofacial implant restoration are comprehensively improved, and the whole process from data to decision making is intelligentized.

The invention discloses a variable convolution UNet tooth image segmentation method and system fusing an attention mechanism, and belongs to the technical field of medical image processing and computer-aided diagnosis. A tooth image data set is obtained and preprocessed; using the preprocessed data set to train a DSC-UNet segmentation model; the DSC-UNet segmentation model introduces a multi-modal feature fusion module and a deformable convolution module into a UNet framework; and segmenting an input tooth image by using the trained DSC-UNet segmentation model, and outputting a pixel-level segmentation result of the tooth structure. By constructing a multi-level feature interaction mechanism and a deformable convolution module, the capturing capability of the network on tooth edge details and the segmentation robustness of special-shaped teeth are remarkably improved, more reliable technical support is provided for an intelligent dental diagnosis and treatment system, and the method is particularly suitable for high-precision segmentation of complex tooth structures in oral panoramic X-ray films and CBCT images.

The invention discloses a tooth CBCT image super-resolution reconstruction method based on a potential diffusion model, and relates to an image processing and generating technology in the field of computer vision. The in-vivo medical image is trained based on hidden space coding and probability diffusion, so that the network has a better super-resolution effect on the real in-vivo tooth CBCT image.

The present invention relates to the technical field of medical imaging. Disclosed are a method, system and apparatus for oral cavity CBCT ultralow-dose imaging. The method comprises: collection of high-dose oral cavity CBCT image data, data splitting processing, data three-dimensional reconstruction processing, model training, image data inputting, image enhancement, and image data outputting; and the specific process involves: performing three-dimensional reconstruction processing on oral cavity CBCT two-dimensional projection data in an ultralow-dose mode of a patient, so as to obtain ultralow-dose oral cavity CBCT three-dimensional reconstruction image data, inputting same into an oral cavity CBCT ultralow-dose imaging image enhancement network model, and performing outputting to obtain high-quality oral cavity CBCT image data for a subsequent clinical diagnosis and treatment process. The present invention can significantly reduce a radiation dose for imaging and accelerate an imaging speed while ensuring the imaging quality, thereby improving the safety of oral cavity CBCT imaging, and having broad clinical application prospects.

The invention discloses an oral cavity tooth CBCT image segmentation method based on semi-supervised deep learning, and belongs to the technical field of oral diagnosis, and the method comprises the steps: collecting oral cavity CBCT image data, carrying out the preprocessing, and building an oral cavity CBCT image data set containing labels and no labels; a semi-supervised segmentation model is constructed based on a progressive average teacher framework, the semi-supervised segmentation model comprises double groups of teacher-student network structures, and each group comprises two student networks with different structures and a double-teacher network; iterative training is carried out on the semi-supervised segmentation model, the training process comprises a supervised learning stage and a semi-supervised learning stage, and a trained semi-supervised segmentation model is obtained; and taking any oral cavity CBCT image data as input, and performing tooth segmentation based on the trained semi-supervised segmentation model. According to the method, the accuracy of tooth segmentation can be improved, the segmentation effect at the tooth boundary position is enhanced, the tooth segmentation steps are simplified, and the diagnosis efficiency of doctors is improved.

The invention relates to the technical field of medical image diagnosis, and discloses a tooth and fracture line recognition treatment method based on the combination of an AI technology and a CBCT image, and the method comprises the steps: obtaining and preprocessing the CBCT image, and carrying out the multi-scale analysis and recognition of a tooth structure, a microcrack and a fracture line through a first AI model. And the second AI model combines the identification result and the patient characteristics, and generates a personalized treatment scheme through multi-objective optimization. Clinical feedback is used for continuously iteratively optimizing double models, and the diagnosis and treatment precision and effect are improved. The system comprises an image data acquisition unit, an image data preprocessing unit, a tooth and fracture line identification unit, a personalized treatment scheme generation unit and a feedback and optimization unit. Through AI and CBCT image fusion, accurate identification of teeth and fracture lines is realized, a personalized treatment scheme is recommended in combination with individual features of a patient and a multi-objective optimization algorithm, rapid response is realized, a closed-loop feedback mechanism continuous optimization model is established, and diagnosis and treatment precision, efficiency and individualization level are remarkably improved.

The invention relates to the technical field of digital medical treatment, and discloses a dental three-dimensional identification and model generation method based on CBCT and deep learning, and the method comprises the steps: carrying out the preprocessing of obtained CBCT image data; the preprocessed CBCT image data is segmented, and the segmentation step comprises the substeps that a tooth three-dimensional model, an alveolar bone three-dimensional model and a mandibular neural tube three-dimensional model are obtained by adopting a two-stage cascade deep learning network; based on the tooth three-dimensional model and the alveolar bone three-dimensional model, obtaining a periodontal membrane three-dimensional model through geometric and Boolean operation; a tooth root area in the tooth three-dimensional model is positioned, and a self-adaptive threshold method is adopted for CBCT image data corresponding to the tooth root area, and a root canal three-dimensional model is recognized and generated; based on at least one of the obtained three-dimensional models, a personalized clinical application model is generated, high-precision automatic segmentation of the dental complex anatomical structure is achieved, and a reliable digital model basis is provided for personalized precision medical treatment.

The invention provides a periodontal problem data positioning method based on a CBCT image. The method comprises the following steps: acquiring a CBCT original image of an oral cavity area of a target object, then filtering an original frequency domain image corresponding to the CBCT original image by using a high-pass filter to generate a filtered frequency domain image, generating a CBCT processed image according to the filtered frequency domain image, and then enhancing the CBCT processed image to generate a CBCT enhanced image. The CBCT processing image and the CBCT enhanced image are subjected to fusion processing to generate the to-be-recognized image, and then the periodontal problem position information is determined according to the to-be-recognized image, so that the influence of artifacts caused by the metal implant on the image quality is effectively avoided, and the image quality is improved. And accurate positioning of the periodontal problem position of the target object of which the oral cavity area is provided with the metal implant is realized.

The invention provides an auxiliary tooth separation method and system for minimally invasive tooth extraction, and relates to the technical field of digital surgery in stomatology. According to the method, the oral anatomy three-dimensional electronic model is obtained by performing deep learning segmentation and three-dimensional reconstruction on the preoperative CBCT image, and the real-time spatial position and posture of the jaw bone are updated in combination with laser oral scanning of the preoperative anterior tooth area. On the basis, a three-dimensional simulation model is constructed, tooth division cutting path planning, interference judgment and dislocation space analysis are completed, precise tooth division cutting is implemented by a three-way mechanical arm along the planned path, and the safe and controllable automatic tooth extraction and tooth division process is completed in cooperation with water cooling and real-time track correction.

The invention discloses a CBCT (Cone Beam Computed Tomography) image distance determination method and device for maxillary sinus and maxillary posterior teeth, a medium and a product, and relates to the field of image processing, the method comprises the following steps: acquiring CBCT images of maxillary sinus and maxillary posterior teeth; according to the CBCT images of the maxillary sinus and the maxillary posterior teeth, performing three-dimensional segmentation of the maxillary sinus and the maxillary posterior teeth by adopting a structure segmentation model; the structure segmentation model is constructed based on a deep learning network; according to a three-dimensional segmentation result, generating a surface mesh model based on a marching cube algorithm; determining point cloud data based on a point cloud generation algorithm according to the surface mesh model; according to the point cloud data, the nearest distance between the maxillary sinus and the maxillary posterior teeth and the corresponding nearest apical point of the maxillary posterior teeth are determined; and determining the position relationship between the maxillary sinus and the maxillary posterior teeth according to the nearest distance, and classifying the position relationship. The distance between the maxillary sinus and the maxillary posterior tooth can be efficiently and accurately measured, and the position relation can be judged.

The invention relates to the technical field of dental crown manufacturing, in particular to an artificial intelligence-based medullary space retention crown manufacturing method and system, and the artificial intelligence-based medullary space retention crown manufacturing method comprises the following steps: obtaining an oral cavity CBCT image and a mouth scanning model; synthesizing the oral cavity CBCT image and the oral scanning model into a synthetic model; inputting the synthetic model into the trained AI model, and generating a medullary space retention crown model by the AI model based on the synthetic model; performing a stress simulation test on the medullary space retention crown model, and regenerating the medullary space retention crown model when the stress of any region of the medullary space retention crown model is greater than a preset value; performing a simulation try-on test on the medullary space retention crown model qualified in the simulation stress test; adjusting the medullary space retention crown model according to the result of the simulation try-on test; inputting the adjusted medullary space retention crown model into a printing device, and printing to generate a semi-finished medullary space retention crown; and curing the semi-finished medullary space retention crown to generate a finished medullary space retention crown. The patient experience is improved.

Disclosed are systems, devices, and methods for navigating a tool inside a luminal network. An exemplary method includes receiving image data of a patient's chest, identifying the patient's lungs, determining locations of a luminal network in the patient's lungs, identifying a target location in the patient's lungs, generating a pathway to the target location, generating a three-dimensional (3D) model of the patient's lungs, the 3D model showing the luminal network in the patient's lungs and the pathway to the target location, determining a location of a tool based on an electromagnetic (EM) sensor included in the tool as the tool is navigated within the patient's chest, displaying a view of the 3D model showing the determined location of the tool, receiving cone beam computed tomography (CBCT) image data of the patient's chest, updating the 3D model based on the CBCT image data, and displaying a view of the updated 3D model.

The invention discloses a tooth instance segmentation method and device based on anatomic form priori, and belongs to the field of computer vision and artificial intelligence. According to the method, model training is carried out on a CBCT image, a real segmentation mask is processed into a three-classification segmentation mask with enamel as a boundary, the three-classification segmentation mask serves as a supervision signal, a network can directly output a prediction segmentation mask containing tooth boundary information in a reasoning stage, and a detection frame is generated accordingly. And after the detection frame is scaled according to the same proportionality coefficient and projected back to the original image, cutting each tooth to obtain a CBCT image of the single tooth. A loss function based on anatomical morphological prior is introduced into segmentation training of a single tooth CBCT image, and a network is constrained through a self-adaptively extracted morphological skeleton, so that a prediction segmentation mask is kept continuous at a root tip part, and the problem of tooth root segmentation fracture is effectively relieved. According to the method, accurate positioning and high-quality segmentation of the tooth instances are realized, so that the segmentation of the tooth instances in the full-mouth CBCT image is quicker and more accurate.

The invention discloses a CBCT system bed board artifact correction method, device and equipment and a storage medium, and relates to the technical field of computed tomography. The method comprises the following steps: scanning first object projection data of an object by a bed board; performing three-dimensional reconstruction on the first object projection data to obtain a first CBCT image; constructing a three-dimensional digital image of the bed board according to the first CBCT image; forward projecting the three-dimensional digital image of the bed board to obtain an independent bed board projection; according to the independent bed board projection, separating the contribution of the bed board from the first object projection data to obtain second object projection data; performing three-dimensional reconstruction on the second object projection data to obtain a second CBCT image; and carrying out image evaluation index judgment until the evaluation index is greater than or equal to a preset threshold value, and obtaining an image without the bed board artifact. According to the method, the three-dimensional digital image of the bed board is constructed through one-time scanning to achieve artifact removal, related problems of secondary scanning are avoided, low-complexity forward projection is adopted to separate bed board contribution, and higher robustness and convenience are achieved.

The invention discloses a CBCT image reconstruction method and system based on deep learning, the system comprises an information input module, a CBCT reconstruction network module and an information output module, the reconstruction method is combined with a freezing-unfreezing mechanism to train a CBCT reconstruction network, a reconstructed CBCT image is generated, and in each training process, the CBCT image is subjected to CBCT image reconstruction. Randomly selecting projection data at a projection angle, and performing projection data filtering through the unfrozen projection domain filtering network; projection data at other projection angles are subjected to projection data filtering through the frozen projection domain filtering network, in view of the fact that the projection domain filtering network is frozen, in the forward transmission stage of training, feature maps of all network layers do not need to be temporarily stored so as to be used for updating parameters in follow-up back propagation, and therefore the training efficiency is improved. And a plurality of continuous cross-sectional images are randomly selected to carry out image domain post-processing, so that the input data volume of an image domain filtering network is reduced, and through the two measures, the demand on a video memory during CBCT reconstruction network training is remarkably reduced.

The invention discloses a dual-stage CBCT and oral scanning tooth registration method and system, and relates to the field of tooth three-dimensional digital model registration, and the method comprises the following steps: extracting a tooth voxel structure in a CBCT image and a dental crown grid structure in an oral scanning image through a segmentation network; a coarse-to-fine depth registration network is introduced, geometric feature coding and feature matching are carried out on super-points obtained through multi-resolution down-sampling, local matching is carried out on a dense point set in a super-point neighborhood, a rigid transformation matrix is calculated in parallel, and an optimal registration result is screened out from the rigid transformation matrix; and dividing a registration object into a plurality of three-tooth groups containing three adjacent teeth, and performing local iteration alignment based on an initial registration result. The method still has higher robustness and accuracy under the complex conditions of large cross-modal difference, high input point cloud density and the like, is shorter in time consumption, and provides reliable data basis and automatic support for clinical scenes such as orthodontics and implantation.

The invention relates to the technical field of medical image processing and artificial intelligence diagnosis, in particular to an oral and maxillofacial surgery image recognition and diagnosis method and system based on deep learning, and the method comprises the following steps: multi-modal image collection and cooperative preprocessing: collecting an oral and maxillofacial surgery CBCT image, a cone beam CT curved surface tomography image, an oral endoscope image and an ultrasonic image, a standardized multi-modal image data set is obtained through inter-modal registration and an adaptive enhancement algorithm; according to the method, a traditional diagnosis framework of'single-mode image + manual film reading 'is broken through, and a three-order diagnosis logic of'multi-mode image cooperative enhancement-cross-scale feature dynamic fusion-focus typing and risk hierarchical linkage' is innovatively provided; and accurate identification, typing and malignant transformation risk prediction of common oral and maxillofacial surgery diseases (such as jaw cyst, wisdom tooth impediment, temporomandibular joint disorder and maxillofacial tumor) are realized.

The invention provides a non-paired graph unsupervised conversion method and system based on an anatomical fidelity Schrodinger bridge. The method comprises the following steps: acquiring a non-paired CBCT image set and a CT image set; based on a preset interpolation mechanism IPM, selecting an image sample from the CBCT image set for iterative processing, and generating an intermediate state sample; predicting the intermediate state sample through a generator network to obtain a synthesized CT image; based on the synthesized CT image and a real CT image randomly extracted from the CT image set, constructing a loss function to drive a training generator network; and converting the input CBCT image into a synthetic CT image by using the trained generator network. According to the method, the distribution difference between the CBCT image and the CT image is minimized by using the optimal transmission framework of entropy regularization, anatomical details are captured and fused under multiple scales, it is ensured that key anatomical features of the generated CT image are accurately reserved, and the accuracy of the CBCT image is improved. The method can effectively solve the problem that the modal conversion precision and the anatomical structure are lost due to lack of pairing data in an existing method.

The present application relates to the technical field of dental diagnosis, and particularly relates to a periodontal disease precise quantitative evaluation method based on IOS images and CBCT images, comprising the following steps: performing image segmentation on an intraoral scanning IOS image to obtain an IOS segmentation result of each tooth; performing image segmentation on a CBCT image to obtain a CBCT segmentation result of each tooth; performing preprocessing and registration on the IOS segmentation result and the CBCT segmentation result to obtain a data fusion model; obtaining a gum contour and a tooth long axis according to the data fusion model, obtaining a gum contour point according to the gum contour, obtaining a alveolar bone contour on the alveolar bone along the direction of the tooth long axis and obtaining a GBD distance according to the gum contour point; and evaluating periodontal disease according to the GBD distance. According to the present application, the data fusion model is obtained according to the segmented IOS result and the CBCT result, the gum contour and the tooth long axis are obtained through the data fusion model, the GBD distance of the gum contour and the alveolar bone contour is measured along the direction of the tooth long axis, and the periodontal disease health condition is evaluated.

The invention discloses an IOS image and CBCT image registration method based on particle swarm and single-tooth optimization, and belongs to the field of image data processing, and the method comprises the following steps: respectively obtaining tooth semantic segmentation results and point cloud data of an IOS image and a CBCT image through preprocessing and segmentation steps; introducing a particle swarm optimization algorithm, and performing iterative search and optimization on six-degree-of-freedom rigid transformation parameters between the IOS image data and the CBCT image data so as to complete global initial registration of the dental arch scale; and on the basis of a result after global registration, a registration object is divided into a plurality of local units based on a single tooth, and local iteration alignment is performed on each unit, so that tiny dislocation on the single tooth is eliminated, and the overall registration effect is optimized. According to the method, on the basis of processing cross-modal difference and realizing global registration, the registration precision of a single tooth level is further ensured through a local optimization mechanism, and reliable automatic registration support is provided for clinical scenes such as orthodontics and implantation.

An alveolar bone structure three-dimensional key point positioning method based on deep learning and rule constraint comprises the following steps: acquiring a maxillofacial region CBCT image data set, cutting CBCT data, manually marking the cut CBCT data, and dividing the CBCT data into a training set, a test set and a verification set; the data set is preprocessed, and data enhancement is carried out in a random intensity shifting and scaling mode; constructing an alveolar bone coordinate network model, wherein the network architecture mainly comprises an appearance branch and a spatial configuration branch; adopting the training set to train the alveolar bone coordinate network model, obtaining each parameter of the network model, and obtaining a trained alveolar bone coordinate network model; and testing the trained alveolar bone coordinate network model by adopting a test data set, and evaluating a test result. The method aims at solving the problems of anatomy prior deficiency, insufficient long-range dependence modeling and weak small sample generalization ability in the prior art, and high-precision alveolar bone key point positioning is realized.

The invention discloses a clinical automatic diagnosis and treatment platform for temporomandibular joint disorder, and relates to the field of medical instruments. The platform comprises a patient subjective symptom evaluation system, a clinical diagnosis system, an image diagnosis system and a treatment scheme making system. The patient subjective symptom evaluation system is used for automatically obtaining a subjective feeling diagnosis result of the patient according to a subjective feeling questionnaire filled by the patient; the clinical diagnosis system is used for automatically obtaining a clinical diagnosis result of the patient according to the past medical history and clinical examination of the patient; the image diagnosis system is used for automatically obtaining an image diagnosis result of the patient according to the CBCT image and the MRI image of the patient; the treatment scheme making system is used for automatically making a treatment scheme. According to the platform constructed by the invention, subjective evaluation information of a patient, a clinical automatic diagnosis result and an imaging automatic diagnosis result are fused, a treatment scheme is automatically formulated, temporomandibular joint disorder can be found and diagnosed in time, and influences on treatment such as orthodontic treatment, orthognathic treatment, repair and implantation are avoided.