124 results about "Cbct image" patented technology

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 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 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 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 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 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 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.

The invention discloses an ultra-sparse CBCT (cone beam computed tomography) reconstruction method, system and equipment based on an ordinary differential equation, belongs to CBCT reconstruction in the field of artificial intelligence, and aims to solve the technical problem of low quality of CBCT reconstructed images. The method comprises the following steps: acquiring sample data, preprocessing the data, constructing and training a CBCT-CT nonlinear relation reconstruction model, and performing real-time reconstruction; during preprocessing, converting the three-dimensional image volume data into simulated X-ray projection data, and reconstructing the simulated X-ray projection data by adopting an FDK reconstruction algorithm to obtain an FDK-CBCT image; the CBCT-CT nonlinear relation reconstruction model comprises an encoder, a NODE module and a decoder; in the training process, the CBCT-CT nonlinear relation reconstruction model is trained through the obtained CT sample image and the FDK-CBCT image. In the reconstruction model, through continuous evolution of NODE modeling image features, the model can model a continuous evolution mapping process from a sparse low-quality image to a high-quality CT image during training, so that stripe artifacts and structural distortion do not easily exist in the reconstructed image, and the reconstruction quality is high.

The application discloses a kind of based on CBCT image data evaluation implant stability classification model and the construction method and device thereof.First, through the Mobilenetv2-DeepLabV3+ network after training in cross-sectional image implant is segmented, then in combination with the knowledge of oral implantology and segmentation result corresponding implant around bone image is extracted on cross-sectional image, finally using the deep residual network Resnet-50 after training is completed classification, obtains implant stability evaluation result.Test evaluation, the model of the application has higher diagnostic performance, the time consumption of evaluation is short, accuracy is high (>90%), can be effectively used for implant stability evaluation.The classification model for evaluating implant stability of the application has important significance for better guiding further implant restoration treatment, and provides important reference for artificial intelligence used for image diagnosis.

A method, system, and device for ultra-low-dose imaging of oral cone-beam computed tomography (CBCT) are provided. The method encompasses high-dose oral CBCT image data acquisition, data segmentation processing, three-dimensional (3D) reconstruction processing, model training, image data input, image enhancement, and image data output. Specifically, the process involves performing 3D reconstruction on 2D projection data of oral CBCT acquired in ultra-low-dose mode to obtain ultra-low-dose oral CBCT 3D reconstructed image data. This ultra-low-dose oral CBCT 3D reconstructed image data is then input into the oral CBCT ultra-low-dose imaging enhancement network model, from which high-quality oral CBCT image data is output for subsequent clinical diagnosis and treatment processes. This method significantly reduces the radiation dose of imaging while ensuring imaging quality, accelerates imaging speed, and enhances the safety of oral CBCT imaging, holding broad clinical application prospects.

The application provides an X-ray beam light baffle and a low-dose CBCT imaging method. First, part of data is taken out from a training set, so that the number of remaining CBCT images and registered CT images is the same, and imbalance of training data is avoided. Secondly, the taken-out data is pre-trained in a self-supervised manner, so that the difficulty in network training caused by the random initialization of the Transformer added to the CycleGAN is avoided. Finally, the pre-trained Transformer is added to the CycleGAN to obtain a hybrid network, and the remaining data is used for unsupervised training, so that the advantages of the Transformer and the convolution are combined, and the ability of the network to enhance the CBCT image quality is improved.

The present application relates to the technical field of medical image processing, and particularly relates to a CBCT data enhancement method and system based on anatomical region constraint and physical consistency; a human body region mask is acquired and a data enhancement model only exerting enhancement disturbance on the human body anatomical tissue region is constructed, a plurality of enhancement disturbances conforming to the CBCT imaging physical mechanism are combined, the CBCT data after enhancement is strictly limited to the anatomical range, non-physical disturbance introduced by the air region is avoided, and the CBCT data after enhancement is close to the real imaging degradation process; at least one enhancement disturbance including gray scale shift, Gamma nonlinear transformation, compound noise, low-frequency scattering field and stripe artifact is executed on the CBCT image after preprocessing, real imaging degradation factors such as device calibration error, detector nonlinear response, electronic noise, photon statistical noise, scattering effect and reconstruction artifact can be simulated, and the adaptability and robustness of the model to different devices, scanning parameters and patient individuals are significantly improved.

Embodiments of the present application disclose a method and device for extracting root canal feature morphology based on CBCT data, equipment and medium, belong to the field of oral medicine and medical image technology, wherein the method comprises: filtering and denoising CBCT images and performing pulp segmentation processing, and performing surface rendering three-dimensional reconstruction on tooth root canals to generate a three-dimensional model; based on the regular point cloud data in the three-dimensional model, a point-surface method is used to realize surface reconstruction to generate a root canal entity model; the root canal entity model is subjected to coordinate alignment processing in a three-dimensional space, and the approximate center line of the fitted root canal is used to measure the root canal length, root canal opening direction, root canal curvature and pulp depth. The present application quickly reconstructs the corresponding three-dimensional model based on the CBCT tomographic images obtained by scanning the teeth of a patient, and obtains relevant data such as pulp depth and root canal opening size from the three-dimensional model of the teeth to assist the performance of oral surgery.

The invention discloses an offset scanning CBCT image reconstruction method. The method comprises the following steps: virtual detector conversion of projection data; carrying out geometric weighting on the data; carrying out data continuation and filtering; parker weighting is carried out on the data; and reconstructing bias back projection. According to the invention, aiming at the scanning mode of radiation source and detector bias, the FDK algorithm cannot be directly used for reconstruction, the virtual detector is introduced to convert the scanning mode into the scanning mode of detector bias, the data is subjected to continuation processing to solve the problem of data truncation, and the filtered data is subjected to Parker weighting aiming at the repeated scanning area to solve the problem of bias artifacts. And finally, carrying out three-dimensional reconstruction by adopting an FDK reconstruction method. The reconstruction method is high in speed, and the obtained cross-sectional image is high in definition.

The invention discloses a CT image-oriented implant space attitude estimation method, and belongs to the technical field of biomedical engineering and computer science. The method comprises the following steps: extracting a target three-dimensional model based on a CBCT image; the CBCT image is obtained by performing cone beam scanning on a target part where an implant is placed during or after an operation; obtaining an implant theoretical model pre-constructed for the implant; the mass center of the target three-dimensional model and the mass center of the implant theoretical model are calculated respectively, and the mass center of the target three-dimensional model and the mass center of the implant theoretical model are aligned; registering the target three-dimensional model and the implant theoretical model of which the mass centers are aligned by utilizing a main shaft rotating mode; and evaluating the registration precision based on multi-plane projection and similarity measurement, and outputting spatial attitude parameters of the implant. According to the method, the registration speed can be increased, the calculation complexity in the registration process is reduced, and the space registration precision can be further improved through a two-stage optimization strategy of centroid alignment and main shaft rotation.

This application belongs to the field of image processing and relates to a skin extraction method and system. The method includes: performing nonlinear regression fitting on the prior anatomical features of skin tissue in historical oral images to obtain a skin cutting threshold; acquiring a CBCT image of the oral cavity to be processed; extracting boundary features from the cross-sectional image layers of the CBCT image to be processed based on the skin cutting threshold to obtain a dynamic candidate masking region feature map; applying inter-layer geometric consistency constraints to the dynamic candidate masking region feature map to generate a target skin masking region; extracting the original pixel values ​​within the target skin masking region and suppressing their background values; performing isosurface evolution processing on the suppressed original pixel values ​​to generate an isosurface grid; and performing patch validity screening on the isosurface grid to generate the skin extraction result. This application can reduce the interference of irrelevant data on the extraction result during the skin extraction process and ensure the quality of skin tissue extraction as much as possible.

The application discloses a CBCT metal artifact suppression method based on an unsupervised preoperative prior repair network and belongs to the technical field of computer image processing. The method comprises the following steps: inputting a preoperative prior CBCT image and a CBCT image to be repaired into a generator after registration, respectively extracting anatomical structure features and non-metal region detail features by using two encoders of a double-flow feature extraction network, fusing the features, and then performing repair reconstruction on the CBCT image by using a decoder; inputting the result into a discriminator to perform authenticity discrimination and metal region discrimination and generate a fused prediction image; taking the prediction image information as a supervision signal to optimize the adversarial loss between the generator and the discriminator and the image similarity loss between the repaired image and the image to be repaired, and performing joint adversarial training on the discriminator and the generator; and processing an intraoperative CBCT image to be repaired by using the trained generator to realize more efficient metal artifact suppression.

This invention provides an artificial intelligence-based method and system for identifying jaw cysts. The method includes: identifying jaw cyst lesion regions in oral CBCT images; extracting image features from the region with the largest connected component as the region of interest for the cyst lesion; identifying the location regions of each tooth in the image; selecting the target tooth closest to the jaw cyst lesion region; constructing a first spatial feature of the jaw cyst lesion region to characterize whether the coordinates of the center point of the jaw cyst lesion region overlap with the coordinates of the location region of the target tooth; identifying the root apical functional region of interest of the target tooth and extracting its image features; identifying the midline region of the image; constructing a second spatial feature of the jaw cyst lesion region to characterize whether the jaw cyst lesion region overlaps with the midline region of the image; concatenating the features to generate a joint feature vector; and inputting the joint feature vector into a machine learning model to identify the lesion nature of the jaw cyst lesion.

The application discloses a CBCT-based child obstructive sleep apnea syndrome identification system and relates to the technical field of medical networks, and comprises the following steps: acquiring a CBCT image and physiological characteristic parameters of a child, performing airway segmentation and reconstructing an airway surface model; establishing an airway respiratory flow field based on the model, setting boundary conditions for simulation, extracting dynamic characteristics to construct first discriminant characteristics; inputting the first discriminant characteristics into a classification model to determine whether the first discriminant characteristics are in a fuzzy discriminant interval; if the first discriminant characteristics are not in the interval, directly outputting an obstructive sleep apnea syndrome type; if the first discriminant characteristics are in the interval, extracting airway cross-section structure information, fusing the first discriminant characteristics to obtain second discriminant characteristics; and matching the characteristics with preset rules to determine whether to adopt enhanced classification or structure registration for secondary discrimination, finally outputting a child apnea syndrome type, and improving the reliability and accuracy of the final diagnosis result.

The invention relates to the field of oral digitization, and provides a three-dimensional form automatic reconstruction method and system for a mandible defect. The method comprises the following steps: importing CBCT image data of a patient with the mandible defect into three-dimensional reconstruction software, and extracting hard tissues of the mandible through threshold segmentation to obtain three-dimensional data of the mandible defect; obtaining structured mandible three-dimensional template data; performing size normalization and initial alignment on the structured mandible three-dimensional template data and the mandible defect three-dimensional data to obtain initial alignment template data; gradually deforming and registering the initial alignment template data to the mandible defect three-dimensional data based on a non-rigid registration algorithm added with symmetry constraint to obtain template deformation registration data; and outputting the template deformation registration data as a mandible defect three-dimensional reconstruction result. According to the method, the automation degree and efficiency of three-dimensional reconstruction of the mandible defect are improved.

An automatic detection method for CBCT cephalometric landmarks based on multi-geometric guidance and specific perceptual coding includes the following steps: Step S1, downsampling the 3D CBCT image and obtaining a preliminary coordinate set through a coarse localization network; Step S2, cropping image blocks centered on the coordinates and extracting local features through a visual encoder containing a shared basic encoder and a low-rank adapter; Step S3, calculating the relative position matrix of the landmarks, encoding spatial relationships using radial basis functions, and constructing a multi-anatomical heterogeneous map; Step S4, inputting visual and edge features into a multi-geometric guidance Transformer, fusing global constraints and updating features using an attention mechanism; Step S5, extracting directional geometric relationships using spherical harmonic functions to construct higher-order update terms, and dynamically updating the heterogeneous map using a gated residual mechanism; Step S6, predicting coordinate offsets through a multi-layer network and performing iterative optimization to output high-precision 3D coordinates. This method significantly improves detection accuracy and robustness.

The present disclosure provides a computer program product, a nasal septum deviation identification device and equipment, and relates to the technical field of artificial intelligence. In some embodiments of the present disclosure, a nasal CBCT image photographed by a cone beam CT device is obtained; the nasal CBCT image is input into a nasal septum detection model, target detection is performed on the nasal CBCT image, and a nasal septum region image corresponding to the nasal CBCT image is obtained; the nasal septum region image is input into a nasal septum deviation classification model, nasal septum deviation classification is performed on the nasal septum region image, and a nasal septum deviation result is obtained; the nasal septum region image and the nasal septum deviation result are sent to a film reading terminal, so that the film reading terminal displays the nasal septum region image and the nasal septum deviation result; by means of the nasal septum detection model and the nasal septum deviation classification model, the present disclosure can automatically read the three-dimensional CBCT image, and compared with the existing artificial reading method, the reading efficiency of the nasal CBCT image is improved, and the reading accuracy is improved.

The present application relates to image correction technology, disclose a kind of CBCT image bone hardening artifact correction method, comprising: the high-energy projection image of target object and low-energy projection is collected and is calculated after image reconstruction to obtain preliminary artifact-free image, based on threshold segmentation from preliminary artifact-free image extraction bone image and soft tissue image and calculate skeleton weight, artifact image of target object is used as training input data, bone image and soft tissue image are used as reference data, and dual-channel artifact correction model is trained, and preprocessed bone image and preprocessed soft tissue image are output according to the bone hardening artifact image to be processed according to dual-channel artifact correction model, according to the preprocessed bone image and preprocessed soft tissue image are weighted fusion according to skeleton weight, and artifact-free reconstruction image is obtained.The present application also proposes a kind of CBCT image bone hardening artifact correction device, electronic equipment and storage medium.The present application can improve the effect of image artifact correction.

The application provides a CBCT-to-CT method and system based on a physical alignment unsupervised diffusion model, an electronic device and a storage medium. The method comprises the following steps: acquiring non-paired source domain CBCT data and target domain CT data of a patient; constructing and initializing a differentiable physical forward degradation model; training a fractional-based unsupervised diffusion model based on the target domain CT data; obtaining prior data distribution of the target domain; in the reverse generation stage of the diffusion model, introducing a target CBCT image as a condition; calculating a physical alignment gradient by using the differentiable physical forward degradation model; and injecting the physical alignment gradient into the prior fractional function sampled reversely by using a frequency domain decoupling strategy, so as to obtain a generated CT image aligned with the anatomical structure of the input CBCT image. The application seamlessly embeds the physical degradation process of X-ray imaging into the generation manifold of the diffusion model, solves the problems of anatomical structure deformation and residual artifacts in the existing unsupervised image conversion, and realizes high-fidelity and high-precision CBCT-to-CT generation.

An artificial intelligence-based endocrown manufacturing method and system is provided. [Solution] The artificial intelligence-based manufacturing method for endocrowns includes the steps of acquiring an oral CBCT image and an intraoral scan model, synthesizing the oral CBCT image and the intraoral scan model to generate a composite model, inputting the composite model into a trained AI model and generating an endocrown model based on the composite model using the AI ​​model, conducting a simulation strength test on the endocrown model and regenerating the endocrown model if the stress in any region of the endocrown model is greater than a predetermined value, and conducting a simulation trial fitting test on the endocrown model that passes the simulation strength test.

The invention discloses a CBCT scattering correction method based on dual-domain interaction and multi-scale wavelet transform, and the method comprises the steps: inputting original scattering-containing CBCT projection data into a preliminary projection correction network, and obtaining preliminary de-scattering projection data; performing three-dimensional reconstruction on the two types of projection data to obtain an original scattering-containing image and a preliminary correction image; fusing the features through an image domain feature fusion network to generate a fusion correction image; performing forward projection on the fusion correction image to obtain reference projection data, and calculating a difference value between the reference projection data and the original projection data to obtain differential projection data; performing multi-scale two-dimensional discrete wavelet transform on the differential projection data, and optimizing coefficients; performing inverse transformation to obtain a refined scattering diagram; and subtracting the refined scattering image from the original projection data, and reconstructing to obtain a final non-scattering CBCT image. According to the method, depth interaction between the projection domain and the image domain is realized, signals are accurately separated in scattering estimation, and the image quality, the detail retention capability and the robustness of the algorithm are effectively improved.

The application relates to the technical field of medical image processing, in particular to a root canal system three-dimensional reconstruction and isthmus identification method and system based on a CBCT image, which comprises the following steps: acquiring CBCT original image data; pre-processing the CBCT image; performing root canal system three-dimensional reconstruction on the pre-processed CBCT image; performing clustering segmentation based on a differential geometry theory on root canal cross sections in the root canal system three-dimensional reconstruction result to extract isthmus features, wherein the clustering segmentation based on the differential geometry theory comprises the following steps: super voxel segmentation driven by curvature flow, elliptical fitting and long axis extraction on a Riemann manifold, isthmus feature extraction by combining a geodesic distance field and a Hough transformation; and marking the isthmus of the root canal system three-dimensional reconstruction result by using the isthmus features, wherein the application realizes accurate identification of a root canal isthmus by introducing the differential geometry theory, improves identification accuracy, and significantly improves the success rate of root canal treatment.

This invention discloses a method for registering IOS and CBCT images based on particle swarm optimization and single-tooth optimization, belonging to the field of image data processing. The method includes the following steps: Preprocessing and segmentation steps are used to obtain the semantic segmentation results of teeth from IOS and CBCT images, as well as point cloud data; a particle swarm optimization algorithm is introduced to iteratively search and optimize the six-degree-of-freedom rigid transformation parameters between the IOS and CBCT image data, thereby completing the initial global registration at the dental arch scale; using the globally registered result as a basis, the registration object is divided into multiple local units based on a single tooth, and local iterative alignment is performed on each unit to eliminate minor misalignments on individual teeth and optimize the overall registration effect. This invention, based on handling cross-modal differences and achieving global registration, further ensures registration accuracy at the single-tooth level through a local optimization mechanism, providing reliable automated registration support for clinical scenarios such as orthodontics and implantology.