414 results about "Cbct imaging" 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 provides an auxiliary dental implant generation method based on a diffusion model, and relates to the technical field of stomatology, and the method comprises the steps: obtaining historical patient oral cavity CBCT image data, carrying out the preprocessing of the historical patient oral cavity CBCT image data, obtaining a CBCT image data set, training a multi-task segmentation network through the CBCT image data set, and obtaining a CBCT image data set; obtaining an intra-oral tissue segmentation result in the segmentation network; respectively training a detection network from three dimensions of a cross section, a coronal plane and a sagittal plane by using an intraoral tissue segmentation result; obtaining detection results in three directions of a cross section, a coronal plane and a sagittal plane by using a detection network; fusing the detection results in the three directions of the cross section, the coronal plane and the sagittal plane, constructing a three-dimensional bounding box by using a majority voting algorithm, and obtaining an edentulous region; the intraoral segmentation result and the edentulous area serve as prompt information, the network is guided through the iteration process to generate the post-implantation effect, the implantation effect obtained through the method is high in accuracy, and a more accurate auxiliary tool can be provided for stomatology.

The invention discloses a mandibular neural tube CBCT panoramic image segmentation network training method based on a Mama architecture, and relates to an image processing technology in the field of computer vision. According to the segmentation network training method, an encoder and decoder architecture is adopted, tubular structure features are extracted by enlarging a receptive field and learning deformation based on feature extraction operation of a tubular convolution kernel, and then a three-vision feature fusion strategy is carried out to further guide a model to supplement attention to key features from different angles; the encoder part models global and multi-scale features through a Mama architecture, and keeps efficient in training and reasoning processes, and finally enables the network to have a more complete segmentation effect on a CBCT image of a tubular structure.

The invention relates to the technical field of tooth diagnosis, in particular to a periodontal disease accurate quantitative evaluation method based on an IOS image and a CBCT image, and the method comprises the following steps: carrying out the image segmentation of an intraoral scanning IOS image, so as to obtain an IOS segmentation result of each tooth; performing image segmentation on the CBCT image to obtain a CBCT segmentation result of each tooth; preprocessing and registering the IOS segmentation result and the CBCT segmentation result to obtain a data fusion model; obtaining a gingival contour and a tooth long axis according to the data fusion model, obtaining gingival contour points according to the gingival contour, obtaining an alveolar bone contour on the alveolar bone along the direction of the tooth long axis by the gingival contour points, and obtaining a GBD distance; and evaluating the periodontal disease according to the GBD distance. According to the method, the data fusion model is obtained according to the IOS segmentation result and the CBCT result, the gingival contour and the tooth long axis are obtained through the data fusion model, and the GBD distance between the gingival contour and the alveolar bone contour is measured in the direction of the tooth long axis so as to evaluate the health condition of the periodontal disease.

The invention relates to a CBCT image tooth segmentation method. The method comprises the following steps: generating a two-dimensional oral cavity panoramic film from a CBCT image; carrying out tooth two-dimensional segmentation and tooth ID classification on the two-dimensional oral cavity panoramic film by adopting a deep learning algorithm; calculating a key point coordinate of a two-dimensional single tooth and mapping the key point coordinate to a three-dimensional CBCT image coordinate; respectively cutting the CBCT image to obtain an area of each tooth; segmenting each three-dimensional tooth region by adopting a deep learning algorithm; and assigning ID to all the tooth segmentation results, and splicing to obtain a three-dimensional tooth segmentation result. According to the method, accurate segmentation of the tooth area in the CBCT image can be realized, complex conditions such as tight tooth arrangement, similar shapes and artifact interference can be effectively handled, the problems of tooth segmentation result fusion, ID confusion and metal artifact interference can be effectively solved, and resource consumption is remarkably reduced by determining the tooth key points on the panoramic film, so that the tooth segmentation efficiency is improved. The overall segmentation efficiency is improved, and the segmentation time is shortened, so that the accuracy of the segmentation result and the processing speed are improved.

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 virtual dynamic three-dimensional image fusion method and system based on pre-registration, and the method comprises the steps: obtaining a static three-dimensional image of a target region, and a dynamic three-dimensional CBCT image which at least comprises one respiratory cycle; performing breathing phase division on the dynamic three-dimensional CBCT image; obtaining a corresponding three-dimensional image frame on each phase node; registering the plurality of three-dimensional image frames obtained by division with the static three-dimensional image in sequence to obtain a single-frame deformation field; collecting breathing signals of a patient in real time during an operation; performing interpolation calculation on the single-frame deformation field based on the respiratory signal, and generating a complete deformation field in the whole respiratory cycle in real time; and applying the complete deformation field obtained by interpolation to the static three-dimensional image, so that the static three-dimensional image is virtually dynamic. Through a method of pre-registration of a pre-operative breathing phase node image and interpolation based on a real-time breathing phase in an operation, the calculation amount during real-time fusion in the operation is reduced, so that ultra-low-delay image fusion is realized.

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 relates to an image correction technology, and discloses a CBCT (cone beam computed tomography) image osteosclerosis artifact correction method, which comprises the following steps: acquiring a high-energy projection image and a low-energy projection image of a target object, performing image reconstruction, and calculating to obtain a primary artifact-free image; extracting a bone image and a soft tissue image from the primary artifact-free image based on threshold segmentation, calculating a bone weight, taking the artifact image of the target object as training input data, taking the bone image and the soft tissue image as reference data, and training to obtain a dual-channel artifact correction model; and outputting a preprocessed bone image and a preprocessed soft tissue image according to the to-be-processed osteosclerosis artifact image by using a dual-channel artifact correction model, and carrying out weighted fusion on the preprocessed bone image and the preprocessed soft tissue image according to a bone weight to obtain an artifact-free reconstructed image. The invention further provides a CBCT image osteosclerosis artifact correction device, electronic equipment and a storage medium. According to the invention, the image artifact correction effect can be improved.

The invention relates to the technical field of medical image processing, in particular to a method and system for identifying odontogenic jaw cyst and tumor based on deep learning, and the method comprises the steps: obtaining original jaw CBCT image data, carrying out the preprocessing, and generating a combined annotation data set containing lesion types, tooth root absorption form annotations and CT value annotations; constructing a multi-branch neural network model, and training the multi-branch neural network model by using the joint labeling data set to obtain an odontogenic jaw lesion recognition model; and obtaining jaw CBCT image data of a target patient, inputting the jaw CBCT image data into the odontogenic jaw lesion recognition model, and outputting lesion type classification results, tooth root absorption forms and bone destruction volumes. The diagnosis capability of minimal lesions can be improved, missed diagnosis is reduced, whole-process auxiliary diagnosis from qualitative diagnosis to quantitative analysis is realized, and the diagnosis speed is increased.

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 CBCT image recognition method and system based on tooth calcification, and relates to the technical field of image recognition. Comprising the following steps: acquiring an oral cavity CBCT image; determining a tooth area image according to the oral cavity CBCT image; preprocessing the tooth area image; performing image enhancement on the preprocessed tooth region image to obtain an enhanced tooth region image; extracting edge calcification features of the enhanced tooth region image by using a canny operator; and identifying the extracted edge tooth calcification features through a U-net tooth calcification identification model. According to the method, the Canny operator is adopted to perform feature extraction, and the Canny is improved in a gradient magnitude calculation method, so that the Canny feature detection can be suitable for more scenes.

The invention discloses a method for predicting change of an anatomical structure in a radiotherapy fractional interval of a radiotherapy patient, and belongs to the technical field of medical treatment. Comprising the steps of data collection, radiomics feature extraction and analysis, deep learning model training and optimization, test set verification model, clinical verification set test, clinical verification set calculation and analysis, model final output result prediction and the like. According to the method, on the basis of MR images, positioning CT images and radiotherapy CBCT images, a prediction model of anatomical structure changes during radiotherapy is constructed and optimized in a radiomics feature analysis and deep learning mode, and on the basis of individual features of a specific patient, anatomical images of the specific patient in future fractional treatment are predicted; a basis is provided for adopting a more active and optimized self-adaptive radiotherapy triggering mechanism, and a more accurate and timely personalized radiotherapy scheme is provided for a patient.

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 discloses an oral cavity CBCT image classification method and device, electronic equipment and a storage medium, and the method comprises the steps: inputting a to-be-classified oral cavity CBCT image into an oral cavity CBCT image processing model, so as to enable the oral cavity CBCT image processing model to generate segmentation information in a segmentation sub-model, and determining a classification image corresponding to the to-be-classified oral cavity CBCT image in the classification sub-model according to the segmentation information. Wherein the segmentation sub-model improves a SwinUnet network model, and adds a residual module to improve the learning ability of the network model; the classification sub-model improves an original 3D-Unet network model, and an attention mechanism module is added to enable the network to selectively learn features of a key area. By implementing the method, the specific structure category of the oral cavity CBCT image can be clearly divided, and the accuracy of a classification result is ensured.

The invention relates to the field of image processing, and discloses a dual-energy CBCT (cone beam computed tomography) hardening artifact correction method which comprises the following steps: acquiring a dual-energy original projection image of a scanned object, performing dual-energy image decomposition on the dual-energy original projection image, combining the decomposed images to obtain a virtual single-energy image, and performing CBCT hardening artifact correction on the virtual single-energy image. And calculating a loss value of a preset correction training model by using the virtual single-energy image, optimizing parameters of the correction training model according to the loss value to obtain a dual-energy CBCT hardening artifact correction model, and carrying out deconvolution on the obtained hardening artifact image by using the dual-energy CBCT hardening artifact correction model to obtain a reconstructed image after hardening artifact suppression. The quality of a dual-energy CBCT image generated through CBCT scanning can be improved, and the accuracy of weakening hardening artifacts of the dual-energy CBCT image can be 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 discloses a CBCT (Cone Beam Computed Tomography) mandibular neural tube refined segmentation method based on multi-view feature fusion. The method comprises the following steps: firstly, selecting a CBCT image and a mandibular neural tube label to form a data set; cutting the training set into a plurality of Patches to obtain input data, and encoding the position coordinates of each Patch; secondly, sending each Patch and the corresponding position code into a pre-segmentation network model to obtain a pre-segmentation result, and obtaining a mandibular canal center line by adopting cubic spline interpolation; and finally, carrying out multi-view feature extraction on the CBCT and the mandibular canal center line, carrying out feature fusion through 3D Attention, decoding the obtained features, finally obtaining a segmentation result of the mandibular canal, and carrying out optimization training. According to the method, feature information of multiple visual angles is effectively fused, and the accuracy and integrity of a segmentation result are ensured.

The invention discloses a dental CBCT three-dimensional tooth rapid labeling method based on an SAM large model. According to the method, an improved SAM network is utilized to distinguish teeth from periodontal membrane and alveolar bone in a CBCT image, and the method comprises the following specific steps: stage 1, conversion and processing from tooth data to trainable data; stage 2, constructing an improved SAM network model; stage 3, training a network model; and a fourth stage of tooth labeling and three-dimensional visualization based on CBCT data. According to the invention, the Lora fine tuning technology is used to help the SAM network adapt to the image characteristics of the medical image data set, so that the defect that the SAM network is only trained on a natural image originally is overcome. In addition, a CNN bypass network is adopted to make full use of spatial information in a three-dimensional slice and help capture edge information in the slice. And finally, a previous prediction result is ingeniously utilized to help to generate a target box prompt to give full play to a prompt mechanism in the SAM network. According to the method, a better tooth labeling effect can be obtained when the same small amount of labels are used for training.

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.