2307 results about "Anatomical structures" patented technology

A robotic surgical system includes a surgeon consol coupled to a patient consol, and the patient consol coupled to surgical instruments. A surgeon computer is coupled to or at the surgeon consol that is coupled to to one or more surgical instruments. A robotic surgery control system includes an artificial intelligence (AI) system with one or more deep learning algorithms. A feedback loop monitors and collects data from the one or more sensors. One or more cameras provide feedback to the robotic surgical system, and are configured to provide images of an anatomical object in at least a two dimensional (2D) arrangements of pixels / Deep learning algorithms of the AI system distinguish different anatomical objects from the images.

The invention provides an artificial intelligence-based ultrasonic image data classification method and system, and the method comprises the steps: firstly obtaining a real-time ultrasonic scanning signal sequence containing the time sequence change characteristics of a tissue elastic parameter and a hemodynamic parameter, carrying out the noise suppression and motion artifact compensation processing, generating a standardized ultrasonic image sequence, and marking the coordinates of an anatomical boundary; then performing multi-scale anatomical structure decomposition on the ultrasonic image to obtain a local feature map set of different organization levels, inputting the local feature map set into a cascade deep classification network, and realizing cross-frame feature fusion and dynamic weight adjustment through spatial-temporal feature alignment and a multi-granularity attention distribution module to obtain a spatial-temporal feature fusion model; and the abnormal region classification probability distribution and the spatial topological relation graph are output, finally, a multi-modal diagnosis report is generated according to the abnormal region classification probability distribution and the spatial topological relation graph, an interactive three-dimensional visual interface containing risk level labels and treatment suggestions is generated after the multi-modal diagnosis report is compared with historical cases, and ultrasonic image classification accuracy and diagnosis efficiency are improved.

The invention relates to the field of image analysis, in particular to a temporal bone disease classification method and system based on a multi-modal medical image fusion technology. The method comprises the following steps: acquiring a multi-modal image of a patient, performing adaptive distortion correction, and generating a standardized image set; performing layer-by-layer anatomical structure semantic segmentation and multi-modal image fusion on the standardized image set to construct an image fusion framework; according to the image fusion framework, performing intelligent recognition on the fine structure of the temporal bone, and constructing a personalized temporal bone anatomical structure chart; performing tissue function state analysis and digital pathology dynamic simulation based on the personalized temporal bone anatomical structure chart, and constructing a digital pathology model; and performing intelligent pathological feature classification based on the digital pathological model to obtain an intelligent classification report. According to the method, rapid, efficient and accurate temporal bone disease classification is realized.

The invention discloses a multi-modal medical image fusion diagnosis system based on artificial intelligence, and the system comprises the following steps: extracting shared features of CT and MRI images through a convolutional neural network, and mapping the shared features to the same feature space; a two-way step-by-step alignment strategy is adopted, a three-dimensional deformation field matrix is generated, and cross-modal image anatomical structure alignment is achieved; calculating modal feature weights and eliminating distribution differences through an attention mechanism and an adversarial domain adaptation layer; constructing a CT-MRI image block contrast learning task, and optimizing a shared feature encoder; a conditional generative adversarial network is used for generating a false image of a missing mode according to the semantic segmentation map, and data distribution is constrained through a Wasserstein distance; uniform feature extraction of multi-modal medical images is realized through a shared feature encoder, the cross-modal image alignment accuracy is improved in combination with a bidirectional deformation field prediction module, and the comprehensiveness and accuracy of fusion features are enhanced by using a multi-modal feature fusion module.

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 coronary angiography image blood vessel segmentation system based on multi-scale feature fusion. According to the invention, through the innovative design of the adaptive morphological sensing module, the system can dynamically analyze the anatomical structure characteristics of the blood vessel: the differentiable morphological operation layer converts the corrosion expansion operation into a learnable feature extraction process, so that the network can autonomously identify the gradient change and boundary trend of the blood vessel wall; the dynamic nuclear adaptation mechanism adjusts the scale and direction of morphological operation in real time according to the local blood vessel diameter and curvature characteristics, interference of surrounding tissues in a main blood vessel area can be inhibited, and continuous expression can be enhanced for capillary branches. Through deep fusion of dissection driving and data driving, the topological structure integrity of a segmentation result at a blood vessel bifurcation point and a narrow lesion area is remarkably improved, and the common problems of blood vessel fracture and misconnection in a traditional method are effectively avoided.

The invention discloses a medical image super-resolution reconstruction method based on multi-level attention guidance, and the method comprises the following steps: S10, constructing a deep learning network model based on a generative adversarial network architecture, which comprises a generator and a discriminator; the generator is based on an improved U-Net architecture, a hierarchical attention module and a dual-path feature processing module are configured in an encoder and a decoder of the generator, the hierarchical attention module adopts different attention strategies according to network levels to consider structure and texture, and the dual-path feature processing module separates and processes low-frequency and high-frequency information; the generator further comprises a multi-level feature fusion module for integrating the multi-scale features of the decoder, and an attention guide up-sampling module for final enhancement and dimension raising. The discriminator adopts a spectrum normalization U-Net architecture and uses multi-scale features for matching; s20, training the network model by adopting a composite loss function comprising pixels, adversarial, perception and total variation loss; and S30, inputting the low-resolution image into the trained model, and outputting a high-resolution image. According to the method, through deep fusion of multi-level attention and multi-scale feature processing, the image restoration quality can be remarkably improved, the texture detail definition can be enhanced, the anatomical structure accuracy can be ensured, and the noise robustness can be improved.

The invention discloses an intelligent detection method and device for fusing a medical image learning image, and relates to the technical field of medical image processing. The method comprises the following steps: acquiring and preprocessing a bimodal medical image, and extracting a feature map through multi-scale decomposition; constructing a cross-modal correlation model, and setting a modal attention mechanism (embedding anatomical structure prior guidance feature complementation) and a morphological attention mechanism (setting lesion morphological constraint weight); the method comprises the following steps: collecting multiple types of image samples, pairing according to a focus form and an imaging mode to construct a bimodal joint data set, and correlating and labeling to generate a training data set with modal attributes; after a multi-stage iteration training model, inputting the preprocessed image to carry out feature fusion so as to obtain a fused image; and generating a lesion probability graph according to the fused image, positioning a lesion area through multi-threshold segmentation, and outputting a detection result. The system comprises a data acquisition module, a preprocessing module and the like. The method improves the accuracy and reliability of medical image detection, and is suitable for clinical multi-modal image analysis.

The invention discloses an AI-based neurosurgery auxiliary robot visual positioning system, and relates to the field of visual positioning, which comprises the steps of deploying and initializing structured light scanning equipment and near-infrared imaging equipment, carrying out multi-modal image acquisition on a surgical area, and carrying out standardization processing, space-time alignment and fusion on the acquired image. A multi-modal visual acquisition and preprocessing module, a three-dimensional tissue model construction and registration module, a visual-anatomical feature recognition and extraction module, an AI auxiliary positioning and path optimization module, an intraoperative dynamic perception and feedback control module and a target position confirmation and instruction output module are constructed. According to the method, high-precision identification and dynamic modeling of a brain tissue structure are realized, real-time identification, path planning and position correction can be carried out on a key anatomical structure in an operation process, the precision, the intelligent level and the intra-operation response capability of neurosurgery operation are remarkably improved, the operation risk is effectively reduced, and the positioning reliability and the automatic control efficiency are improved.

The invention relates to the field of medical image processing, in particular to a lung cancer PET-CT fusion segmentation method and system based on multi-modal feature comparative learning, and the method comprises the steps: firstly extracting PET and CT image features, projecting the features to a shared semantic space through a semantic guide type symmetric comparative learning architecture, obtaining key region features through a focus adaptive attention sampling mechanism, and carrying out the segmentation of a target region; optimizing feature representation through a cross-modal feature difference self-calibration mechanism, constructing a multi-scale feature pyramid, fusing features of different scales by using a multi-scale hierarchical contrast learning mechanism, and performing self-supervised learning by combining an anatomical guidance self-supervised contrast learning enhancement module and using a CT anatomical structure, so as to further reinforce the features; a high-precision lung cancer lesion segmentation result is generated through a decoder network, the Dice coefficient is increased from 0.78 to 0.91, and the detection rate of lesions below 10 mm is increased from 65% to 87%. A novel efficient and accurate image processing method is provided for lung cancer diagnosis.

The invention provides a medical image segmentation method based on geometry and topology cooperative guidance. The medical image segmentation method comprises the following steps of image preprocessing and data enhancement; a shared encoder; a dual-path cooperative decoder; carrying out multi-mode deformation iterative refining; and a multi-objective composite loss function and an optimization strategy. The method has the beneficial effects that the performance can be remarkably improved: through a unique geometry and topology collaborative refining mechanism, the segmentation precision and the boundary definition are far superior to those in the prior art, the topology correctness of an anatomical structure can be actively maintained and repaired, clinically unacceptable errors are remarkably reduced, and the reliability of a result is improved; in addition, operation can be simplified, stability and generalization are enhanced, and advanced application is promoted.

The invention discloses an image registration and segmentation joint optimization method, system, device and medium, and relates to the technical field of image registration and segmentation, and the method comprises the steps: obtaining an original medical image, and employing a dynamic pairing strategy to obtain a floating image and a fixed image; the method comprises the following steps: constructing an image registration and segmentation joint optimization model, wherein the model comprises an encoder sharing weight, a segmentation decoder, a registration decoder and an uncertainty estimator; and inputting the floating image and the fixed image into an image registration and segmentation joint optimization model to obtain a registered image and a tag thereof, inputting the fixed image and the registered image into an uncertainty estimator to predict the uncertainty of registration, and minimizing a loss function to obtain an updated image registration and segmentation joint optimization model. According to the method, the problem of class imbalance of different anatomical structure labels can be relieved, and the forward action of the layered segmentation feature map in the deformation subfield generation process can be promoted.

Systems and methods are disclosed for creating an interactive tool for determining and displaying a functional relationship between a vascular network and an associated perfused tissue. One method includes receiving a patient-specific vascular model of a patient's anatomy, including at least one vessel of the patient; receiving a patient-specific tissue model, including a tissue region associated with the at least one vessel of the patient; receiving a selected area of the vascular model or a selected area of the tissue model; and generating a display of a region of the tissue model corresponding to the selected area of the vascular model or a display of a portion of the vascular model corresponding to the selected area of the tissue model, respectively.

The invention relates to the technical field of medical image processing, in particular to an artificial intelligence assisted intraoperative imaging method, which comprises the following steps: S1, preprocessing a multi-modal medical image, segmenting and recognizing an anatomical structure by a deep learning model according to the preprocessed image, measuring anatomical parameters based on a segmentation and recognition result, and generating an operation planning path by artificial intelligence according to the anatomical parameters; s2, collecting a C-shaped arm perspective image stream in real time, dynamically tracking space coordinates of a surgical instrument, comparing the position of the instrument with a surgical planned path, calculating offset, and when the offset is greater than an offset threshold, outputting correction guidance through an AR superposition layer; and S3, monitoring an image quality index in real time, dynamically adjusting exposure parameters through a reinforcement learning model, and when a metal implant is detected, switching a dual-energy-spectrum mode and executing an artifact suppression algorithm. According to the method, preoperative precise planning and intraoperative assistance are realized through artificial intelligence, the problems of poor image quality and high radiation risk are solved through technical optimization, and the method has important clinical application value.

The embodiment of the invention discloses an image annotation method and system applied to brain MRI image segmentation, and the method comprises the steps: obtaining a brain MRI image data set of a target object, and the brain MRI image data set comprises original image sequences of a plurality of scanning levels; performing multi-modal feature fusion processing on the original image sequence to generate an enhanced image feature set; calling a multi-layer cascade segmentation network to perform hierarchical feature extraction on the enhanced image feature set to obtain a multi-scale anatomical structure feature map; and performing region boundary optimization processing based on the multi-scale anatomical structure feature map, and generating a marked brain structure segmentation image. Therefore, the boundary of each structure of the brain can be accurately defined, the segmented image is more accurate and clearer, and the image segmentation and marking of the brain MRI image can be accurately and clearer realized.

The invention provides a liver focus three-dimensional modeling method, and belongs to the technical field of image processing based on computer vision. Firstly, a multi-view spatial registration method based on optical flow optimization is designed, pixel-level displacement information of different view images is estimated by calculating an optical flow field, accurate image alignment is achieved, and spatial consistency of three-dimensional reconstruction is improved. And secondly, a three-dimensional reconstruction strategy based on two-dimensional focus segmentation is proposed, the two-dimensional focus segmentation is completed by adopting a lightweight U-Net variant, and a segmentation result is mapped to a three-dimensional space through a voxel probability projection method, so that 3D focus reconstruction is realized, and the calculation cost is reduced. And finally, extracting high-frequency features of the three-dimensional model by adopting a local edge enhancement method based on a Laplacian operator, and strengthening a focus boundary and a key anatomical structure through interpolation optimization, so that the three-dimensional model is more accurate and clearer. Compared with a traditional method, the method has the advantages that the mode of purely depending on image superposition is avoided, and the accuracy of three-dimensional modeling is improved.

A clinical vertebra image segmentation method for assisting pedicle screw placement surgery, said method comprising: constructing a VerseDiff-UNet end-to-end framework, the framework being integrated with a denoising diffusion probabilistic model (DDPM); combining a noise-added image with a marked mask by using the VerseDiff-UNet framework, and guiding a diffusion direction toward a target region; and introducing a shape priors module on the basis of the DDPM, and extracting structural semantic information from an input spine image. In order to capture specific anatomical prior information in a medical image, the shape priors module is combined and the module effectively extracts the structural semantic information from the input spine image, thereby enabling more accurate anatomical structure segmentation, and facilitating accurate diagnosis and treatment of spinal disorders.

The invention belongs to the technical field of medical image processing, and particularly relates to an automatic positioning and typing method for cervical posterior longitudinal ligament ossification, which is characterized in that ossification focus spatial distribution characteristics of CT images and spinal cord morphological parameters of MRI images are synchronously analyzed based on CT and MRI images, a vertebral body-ossification-spinal cord spatial relationship is explicitly modeled through anatomical structure topological constraint, and a cervical vertebra-ossification-spinal cord spatial relationship is obtained. Diffuse calcification artifacts, heterogeneity signal interference and multi-modal data registration deviation are overcome, accurate positioning and typing diagnosis of the ossification are achieved, multi-dimensional feature fusion analysis is achieved, the limitation of traditional single iconography index diagnosis is broken through, space continuity and clinical parameters are integrated through a hybrid classifier, and the accuracy of diagnosis is improved. The accuracy and robustness of typing judgment are improved, a structured diagnosis report is automatically generated in the whole process, and the clinical decision-making efficiency and the standardization level are remarkably improved; the principle is scientific and reliable, and the ossification continuity index, the spinal canal invasion rate and the spinal cord compression grading parameters are automatically calculated according to the typing standard.

A system includes a uterine manipulator having a shaft. The uterine manipulator is coupled with the robotic arm. An imaging instrument is operable to provide an image of an exterior of the uterus of the patient. A console includes a display screen and is configured to provide a view from the imaging instrument of the exterior of the uterus of the patient, on the display screen. The console is further configured to provide an indicator on the view from the imaging instrument, on the display screen, the indicator indicating a location of a predefined anatomical structure, the indicator being provided as an overlay on the predefined anatomical structure.

The invention belongs to the technical field of medical image assistance, and particularly relates to an auxiliary stereo imaging system and method for a complex chest trauma operation. By integrating multi-modal image data, real-time three-dimensional space capture, dynamic correction and risk score driven view adjustment, accurate assistance of the thymotomy operation process is achieved, the accuracy and safety of the operation are improved, the operation risk and operation difficulty are remarkably reduced, and the operation efficiency is improved. By acquiring and processing multi-modal image data in real time, a high-precision three-dimensional anatomical structure model containing thymus tissues, a blood vessel network and nerve distribution can be constructed, a visual surgical navigation view is provided for a doctor, meanwhile, a dynamic correction technology is adopted, thymus displacement parameters caused by thoracic cavity respiratory movement of a patient are monitored in real time, and the thoracic cavity of the patient can be accurately and accurately positioned. And on the basis, the real-time three-dimensional space data is dynamically adjusted, so that the spatial synchronization of the image data and the anatomical structure is ensured, and the navigation error caused by respiratory movement is effectively avoided.

The invention provides a digital information processing method for a hospital radiology department, which comprises the following steps: S1, multi-modal image collaborative acquisition and standardization: synchronously acquiring anatomical structure images, functional images and metabolic parameter data of a patient through radiology department imaging equipment, and converting the anatomical structure images, the functional images and the metabolic parameter data into space-time aligned three-dimensional digital matrixes; s2, image quality optimization processing: performing nonlinear contrast enhancement and noise suppression on the original image to improve the signal-to-noise ratio of a target area; s3, dynamic self-adaptive registration: according to the biomechanical characteristics of the organ, fusing the rigid transformation model and the elastic deformation model, and according to the digital information processing method for the hospital radiology department, based on the dynamic registration matrix of the biomechanical model, improving the multi-modal image fusion precision; a deep learning segmentation algorithm fused with morphological constraints improves the focus boundary recognition accuracy; a texture mapping three-dimensional reconstruction technology is mixed, and an anatomical structure and metabolism information are presented at the same time; the invention discloses a structured report automatic generation system based on an attention mechanism.

The invention discloses a thyroid intraoperative real-time navigation method and a thyroid intraoperative real-time navigation system based on multi-mode optical fusion. The thyroid intraoperative real-time navigation method comprises the following steps: outputting visible light through an endoscope and coupling light waves of a narrow-band multispectral light source to irradiate an operative field, exciting parathyroid glands to generate near-infrared fluorescence, and receiving reflected visible light, split light, near-infrared fluorescence and laser speckle signals through an endoscope probe. And separating the composite optical signal into four channels, and respectively generating an anatomical structure color image, a blood vessel spectroscopic image, a parathyroid gland near-infrared fluorescence image and a laser speckle image. Performing decorrelation processing on the laser speckle image to generate a blood flow dynamic pseudo-color decorrelation speckle image; an anatomical structure, a blood vessel center line, a parathyroid gland contour and blood flow dynamic feature points are extracted through a multi-modal registration technology, after affine transformation space alignment is conducted, a comprehensive imaging map containing the anatomical structure, blood vessel distribution and parathyroid gland function marking information is generated through a wavelet fusion algorithm, and intraoperative multi-dimensional real-time tissue navigation is achieved.

The invention discloses a multi-person posture recognition method based on CSI (Channel State Information) and an attention mechanism, and the method comprises the steps: constructing a multi-person posture recognition system MultiFormer based on the CSI and the attention mechanism, designing a double-TokenTransformer architecture of a time-frequency double-domain Token TFDDT, converting an original CSI signal into a time domain Token and a frequency domain Token, maintaining the local feature continuity of the time domain and the frequency domain, and carrying out the recognition of the posture of a plurality of persons. And a multi-stage feature fusion network MSFN is developed to optimize a part thermodynamic diagram PCM and a part associated field PAF, and the fusion of CSI features and an intermediate attitude thermograph is realized through an adaptive channel-space attention mechanism, so that the anatomical consistency is ensured, and a multi-person scene is supported. According to the method, a MultiFormer system is provided and is different from existing CSI imaging processing, an interpretable TFDDT preprocessing method is adopted, the system adopts a multi-stage heat map estimation method to realize global attitude perception, the estimation precision is improved through iterative optimization, a multi-person scene is supported, attitude topology conforming to an anatomical structure is generated, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. The method has the characteristics of privacy protection, low cost and no influence of illumination.

The embodiment of the invention discloses an oral and maxillofacial surgical image recognition and diagnosis method and system based on deep learning, and the method comprises the steps: firstly obtaining an oral and maxillofacial three-dimensional image data set of a target patient, then carrying out the image feature extraction processing of the three-dimensional image data set, and obtaining a hierarchical image feature set; comprising local anatomical structure features and global spatial distribution features, and then calling a pre-trained multi-scale feature fusion network to perform multi-scale feature fusion on the hierarchical image feature set to generate a fusion feature map. And performing focus area identification processing based on the fusion characteristic spectrum, determining position information and form description information of an oral and maxillofacial abnormal area of the target patient, generating a diagnosis report according to the position information and form description information of the oral and maxillofacial abnormal area, and transmitting the diagnosis report to medical terminal equipment for display. Therefore, the accuracy and efficiency of oral and maxillofacial surgery image diagnosis are improved.

The invention discloses an intelligent planning method for a personalized scanning path of an ultrasonic robot, and relates to the field of ultrasonic robos.The intelligent planning method comprises the steps that anatomical structure information and clinical scanning demand information of a to-be-scanned part of a target patient are collected, a patient exclusive basic information set is established, and the anatomical structure information comprises spatial distribution characteristics of all tissues of the part; clinical demand information comprises a preset focus region and a scanning precision index, an exclusive basic information set is established by collecting an anatomical structure of a to-be-scanned part of a patient and the clinical scanning demand information, spatial correlation characteristics are analyzed, and a real-time physiological and anatomical position dynamic correlation rule is embedded to construct a dynamic anatomical correlation model; and individual anatomical dynamic changes of the patient can be accurately matched.

The invention discloses an image segmentation method for evaluating a hepatocellular carcinoma neutron therapy dose, and relates to the technical field of neutron therapy. A 3D U-Net GAN network model is trained through a medical image data set; a generator model and a discriminator model in a 3D U-Net GAN network model are alternately trained by using a real image and a false image randomly generated by the generator model, so that the trained generator model can generate a segmentation result which is more accurate and rich in details for a medical image; the trained discriminator model can more accurately evaluate the authenticity of the output of the generator model, and the trained network can accurately reflect the anatomical structure of the patient, the distribution condition of 10B in the body and the radionuclide dynamics condition when segmenting the medical image, so that the information in the medical image can be accurately and fully displayed, and the medical image segmentation efficiency can be improved. The application is convenient.

The invention provides an ultrasonic image discrimination perception pre-training method based on a cooperative training framework, and relates to the technical field of ultrasonic image analysis, and the method comprises the steps: obtaining an ultrasonic image sequence containing time sequence information; constructing a cooperative training architecture, wherein the two networks are both based on a visual converter and embedded with a time sequence anatomical attention module; inputting the original image into a teacher network, inputting the enhanced image into a student network, and respectively outputting global and local features; calculating an anatomical continuity measure based on the output features of the two-network time sequence anatomical attention module; constructing a total loss function; the regularization weight is dynamically adjusted according to the anatomical continuity measurement, and student network parameters are updated through gradient descent; dynamically calculating an index moving average coefficient according to the anatomical continuity measurement so as to update teacher network parameters; and iterative training is carried out until convergence. According to the method, the time sequence continuity and anatomical structure information contained in the ultrasonic video sequence are fully mined, so that the adaptive optimization of the model training process is realized.

The invention relates to the field of medical image segmentation, and discloses a precision surgery-oriented robot medical image segmentation and feature extraction method, which comprises the steps of constructing a dynamic segmentation network model of a bidirectional attention architecture, and inputting a preprocessing module for feature extraction and generating a multi-scale feature pyramid; the Transform coding branch is used for time sequence feature modeling and outputting a time sequence enhancement feature; the convolutional coding branch is used for enhancing anatomical features and surgical instrument features and outputting spatial enhancement features; the multi-stage feature fusion unit is used for performing multi-stage iterative fusion and outputting final fusion features; the decoding output module is used for decoding and generating pixel-level segmentation masks of the anatomical structure and the surgical instrument; training the dynamic segmentation network model; and performing medical image segmentation and feature extraction based on a medical image video sequence input in real time by using the trained dynamic segmentation network model. Accurate segmentation of anatomical tissues and dynamic instruments in an operation scene is realized.

The invention discloses a failure restoration retreatment decision-making method based on dynamic occlusion analysis, and the method comprises the steps: constructing a four-dimensional virtual biomechanical model which comprises a patient anatomical structure and a dynamic function through multi-modal data fusion, and enabling the model to be embedded into a real three-dimensional mandibular movement track of a patient to simulate functional movement, thereby breaking through a static analysis blind area, and achieving the real three-dimensional mandibular movement of the patient. The missed diagnosis rate of dynamic interference is reduced; based on quantitative indexes and thresholds such as occlusal contact displacement, occlusal contact distribution symmetry and adjustment quantity of the maximum tooth tip staggering position and the median relation position, experience judgment of doctors is replaced, objectivity, repeatability and accuracy of failure repair and retreatment decision are improved, and excessive or insufficient adjustment is avoided; the treatment effect rehearsal is realized through virtual presetting, the irreversible operation risk is avoided, and the number of times of re-visit and the trial and error cost of a patient are reduced; and meanwhile, the four-dimensional virtual model can be connected with transition and design and manufacturing of a final restoration, so that the accuracy, safety and efficiency of retreatment of a failed restoration are improved.

The invention discloses an ultrasonic endoscope navigation system and method based on deep learning, and relates to the field of medical image analysis, and the system comprises a parallel encoder module which is used for extracting local features of an ultrasonic endoscope image through a CNN branch, and capturing global context information through a Transform branch; the channel attention fusion module is used for carrying out adaptive weighted fusion on the extracted features; a decoder module that generates an anatomical structure segmentation mask based on the fused feature; the time sequence processing module is used for receiving the frame-by-frame segmentation result output by the decoder module and realizing time sequence coherence analysis of the ultrasonic endoscope video through a bidirectional LSTM network; and the multi-modal fusion module is used for carrying out registration and fusion on the ultrasonic endoscope images. According to the scheme, a high-reliability artificial intelligence auxiliary tool can be provided for early screening of pancreatic cancer, meanwhile, the learning threshold and clinical application cost of the ultrasonic endoscope technology are reduced, and popularization of the ultrasonic endoscope technology in basic medical institutions is promoted.