164 results about "Multimodal imaging" patented technology

A robotic surgical system includes a robotic manipulator configured to perform surgical procedures under direct surgeon control. A surgical camera system captures real-time intraoperative video. An external imaging interface receives multimodal imaging data, including preoperative and intraoperative data from at least one of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and fluoroscopy. An artificial intelligence (AI module has a trained neural network and a deep learning model trained on multi-institutional annotated surgical datasets, The AI module is configured to execute one or more of: fuse acquired video and imaging data into temporally and spatially coherent anatomical visualizations; generate continuously updating overlays aligned with the surgical field, with segmented anatomical features; projected tissue boundaries, proximity indicators for instruments, and predictive deformation trends; provide dynamic predictive trend visualization indicating zones of future anatomical complexity or risk; register and align preoperative imaging data with intraoperative imaging data in real time; adapt overlay presentation in response to tissue deformation without actuating the robotic manipulate or; and passively augment visual feedback without initiating any autonomous actuation of surgical instruments.

The invention discloses a breast cancer immunohistochemical index and typing prediction method and system. The prediction method comprises the following steps: acquiring a breast ultrasound image and a breast magnetic resonance image of a patient; performing preprocessing and quality control on the mammary gland ultrasonic image and the mammary gland magnetic resonance image; performing breast lesion area segmentation by adopting a deep learning model to obtain segmented lesion areas; based on the segmented lesion area, extracting multi-modal radiomics characteristics of the breast ultrasonic image and the breast magnetic resonance image; fusing the multi-modal radiomics characteristics of the breast ultrasound image and the breast magnetic resonance image, constructing a machine learning model, and performing immunohistochemical index prediction to obtain an immunohistochemical index prediction result; and performing breast cancer molecular typing analysis according to the immunohistochemical index prediction result. By fusing the multi-modal image information, the tumor features can be described more comprehensively, and the accuracy of biomarker prediction is improved.

The invention discloses a multi-modal imaging method and system based on acoustic-thermal coupling, and relates to the field of artificial intelligence, and the method comprises the steps: firstly obtaining thermal radiation data and ultrasonic reflection data of a target equipment region, and obtaining thermal radiation embedded features and ultrasonic embedded features through feature representation learning; performing multi-stage compensation on the thermal radiation embedding characteristic to generate a thermal field compensation reference characteristic of each stage; synchronously carrying out equal-stage multi-stage compensation on the ultrasonic embedded features, coupling the ultrasonic compensation result of the previous stage with the thermal field reference features of the corresponding stage between every two continuous stages of compensation, and combining the ultrasonic embedded features and coupling output in the later stage of compensation; and finally, performing imaging by using the acoustic-thermal coupling characteristics after multi-stage compensation to obtain an acoustic-thermal coupling imaging result fusing the thermal distribution characteristics of the target equipment region and the ultrasonic reflection spectrum characteristics. According to the method, deep fusion of acoustic and thermal information is realized through multi-stage feature compensation and dynamic cross-modal coupling, and the comprehensiveness and accuracy of equipment area detection are improved.

The invention relates to the technical field of die cutting machines, and particularly discloses a die cutting defect detection method and system, and the method comprises the steps: obtaining a bright field image and a polarization image of a to-be-detected film; correcting, registering and integrating the bright field image and the polarization image to obtain multi-channel image input information; utilizing a pre-trained defect positioning model to analyze and obtain a defect area according to the multi-channel image input information, and positioning coordinates of the defect area; extracting defect characteristic information in the defect area; associating the defect characteristic information with production parameter information of the to-be-detected film, and storing the defect characteristic information in a defect database; analyzing the defect information in the defect database regularly or in real time to generate an analysis result; according to the method, a complete defect detection, analysis and feedback closed loop is formed through multi-modal imaging, image processing, model positioning, feature extraction and data association and analysis, and the accuracy and robustness of defect detection are improved.

The invention relates to the technical field of tumor interventional operations, and discloses a visual navigation method based on a tumor interventional operation robot. The method comprises the following steps: acquiring real-time medical image data of a tumor area containing multi-modal imaging information so as to comprehensively present anatomical details; and performing three-dimensional reconstruction on the image data to generate a tumor area three-dimensional anatomical structure model capable of visually displaying a space structure. Key anatomical feature points are extracted based on the model, space coordinates are calculated, a surgical robot intervention path is planned according to the coordinates, and an initial navigation track is generated; and continuously collecting real-time pose data of the robot in an operation, dynamically matching the real-time pose data with the initial navigation trajectory, adjusting motion parameters according to a matching result, and generating a corrected navigation instruction. The method can reflect the intraoperative anatomy condition in real time, dynamically optimize the path, solve the problems that traditional navigation depends on preoperative static images and lacks real-time adjustment, reduce operative complications and improve the treatment effect of patients.

The invention discloses a medical developing marker and a manufacturing method thereof, and relates to the field of medical instruments, the medical developing marker comprises a closed tube, the outer wall of the closed tube is provided with a bionic barb structure, the bionic barb structure is distributed in a flexible zigzag convex manner, and the bionic barb structure and the closed tube are integrally formed through an injection molding process; a closed cavity is arranged in the closed tube, four developing strips are symmetrically arranged in the tube wall of the closed cavity, the tube wall of the region where the developing strips are located is etched to form a weak band, and a developing material layer and a sustained-release medicine layer are sequentially deposited at the weak band. The developing material layer and the sustained-release drug and / or marked nano-particle layer are alternately deposited to form a functional composite layer, and the functional composite layer can directionally rupture to release the therapeutic drug under the action of implantation pressure. The marker integrates the functions of precise development, tissue anchoring and drug sustained release, is adaptive to multi-mode imaging such as ultrasonic imaging, X-ray imaging, CT imaging, MRI imaging and PET-CT imaging, and has remarkable clinical value in precise positioning treatment of solid tumors such as breast cancer and liver cancer.

The invention discloses an intelligent scalp health assessment system based on multi-modal imaging and data fusion, and relates to the technical field of hair quality detection and analysis, comprising multi-source heterogeneous scalp data acquisition and fusion decision, abnormal image and sensor data correction through high-order PDE interpolation and multi-scale filtering, and multi-modal imaging and data fusion. CNN / LSTM and adaptive attention are utilized to extract multi-modal features such as hair follicle morphology, inflammation hot spots and grease fluctuation, multi-head attention and a knowledge graph are fused to generate a weighted decision result, and robust processing on a missing mode is enhanced by means of interpretable visualization and a dynamic completion mechanism; after data acquisition is completed, abnormal readings can be automatically detected, and local interpolation is performed by using a depth prediction model, so that the accuracy and stability of multi-modal decision making are improved; meanwhile, personalized model fine tuning is introduced, longitudinal incremental training and risk prediction are carried out on multiple detection records, and a fine management scheme is provided for long-term monitoring and nursing intervention of scalp health.

The invention discloses a multi-modal imaging system and method for a small animal living body. The system comprises a multi-modal fusion imaging module which is responsible for multi-modal image acquisition and primary processing; the real-time dynamic monitoring module is used for capturing bioluminescence / fluorescence signals in real time through a photon counting detector, receiving optical, nuclear medicine and magnetic resonance data, correcting motion artifacts based on a multi-scale space-time registration engine and adjusting scanning parameters through a real-time pharmacokinetic-physiological feedback mechanism; the low-radiation and biological compatible module is used for automatically optimizing the dosage of a tracer agent according to the weight of the animal, the scanning part and historical data by using a dosage prediction model; and the multi-modal data fusion module is used for performing non-rigid registration of multi-modal images based on a Transform cross-modal registration network, constructing a multi-species pharmacokinetic knowledge graph by utilizing multi-species metabolism chip data, integrating mouse, dog, primate and humanized liver chip data, and mining a cross-species metabolism rule through a graph neural network.

The invention discloses a lumbar disc herniation postoperative recurrence prediction system based on a multi-modal medical image, and belongs to the field of medical images. The lumbar disc herniation postoperative recurrence prediction model based on the multi-modal image is trained by using the lumbar vertebra image set, the feature true value corresponding to each image and the recurrence prediction label corresponding to each patient, and in the training process, model parameters are updated by using a stochastic gradient descent algorithm; and a trained lumbar disc herniation postoperative recurrence prediction model based on the multi-modal image is obtained. According to the method and the system, due to rich image features provided by the multi-modal image, the prediction network trained based on the multi-modal data shows higher accuracy in postoperative recurrence prediction of the lumbar disc herniation.

The invention discloses a multi-mode head and neck tumor segmentation and survival prognosis prediction method. The method comprises the following steps: constructing a DE-LS-UNet model; training the DE-LS-UNet model to obtain a trained DE-LS-UNet model, and inputting the new PET image and the CT image into the trained DE-LS-UNet model to obtain a predicted tumor segmentation mask; extracting multi-modal radiomics features according to the predicted tumor segmentation mask, and fusing clinical features through the multi-modal radiomics features to generate multi-source features of the patient; inputting the multi-source features of the patient into the survival analysis model for training to obtain a trained survival analysis model, and inputting the features of the patient into the trained survival analysis model to obtain a prognosis prediction result. According to the method, a feature extraction and fusion mechanism is provided, and a survival analysis model strategy is combined, so that the stability and prediction performance of survival prognosis modeling can be enhanced while the image segmentation precision is improved.

The invention relates to a prostate tumor thermal therapy device and method, the device comprises particles heating under the action of a magnetic field, and is characterized by further comprising an imaging positioning module, a particle implantation module, a particle fixing module, a magnetic field excitation module and a control and display module, the imaging positioning module comprises a multi-mode imaging device composed of an ultrasonic imaging unit, a CT scanning unit and an MRI imaging unit, and the multi-mode imaging device at least adopts two devices of the ultrasonic imaging unit, the CT scanning unit and the MRI imaging unit to construct a three-dimensional model of the prostate and the tumor. Through the arrangement, particles can be accurately positioned and fixed, the operation precision is improved, and the operation difficulty is reduced.

In some embodiments provided herein is a generative foundation model trained over millions of health system-scale electronic health records along with web-scale medical text corpora to acquire knowledge of both medical practices and theories, and use of the generative model for rare disease diagnosis (including rare ophthalmic, diseases and rare systemic diseases), emergency condition identification (including ophthalmic emergencies and systemic emergencies), complex disease solving ("diagnostic puzzles"), or generating multimodal medical imaging reports (including ophthalmic images and radiology images such as X-rays and CT scans). In some embodiments, the generative model involves the use of language data, for pre-training, language data for supervised finetuning using a instruction tuning approach (e.g., QA pairs), and a human-machine hybrid evaluation strategy. In some embodiments, both the pre-training and supervised finetuning phases involve the use of a particular method of scaling to extend the context window. In some embodiments, the human-machine hybrid evaluation strategy involves language data for automated evaluations, as well as evaluations by generalists and by different specialists (e.g., ophthalmologists and radiologists) of varying levels of experience. In some embodiments, the generative foundation model, MetaGP, is used for unmet clinical needs through integration of medical and multimodal imaging data.

A multimodal imaging unit for depth-surface imaging of a skin region of interest includes an ultrasound imaging transceiver, an optically transparent acoustic deflector, an optical module, and an optical camera sensor. An ultrasound beam emitted towards the acoustic deflector is deflected towards the skin region of interest, and an ultrasound beam reflected from the skin region of interest is returned to the ultrasound imaging transceiver. An optical beam from the optical module is passed through the acoustic deflector to a skin area of the skin region of interest and an optical beam reflected from the skin area is returned to an optical camera sensor through the acoustic deflector. The transceiver and the acoustic deflector are surrounded by an intermediary coupling medium and are hermetically enclosed by a cover provided with an acoustically and optically transparent access port for the ultrasound beams and the optical beams.

The invention discloses a disposable absorbent article defect detection system and method based on AI vision, the system is arranged corresponding to a production line, an image acquisition module in a multi-modal imaging unit is arranged corresponding to a station on the production line so as to acquire an image and preprocess the image, the image acquisition module is arranged corresponding to an encoder, and the encoder is arranged corresponding to the multi-modal imaging unit. The encoder sends out a pulse signal to trigger the image acquisition modules, and all images corresponding to the same product acquired by all the image acquisition modules are associated based on the encoder; the AI processing unit is used for carrying out defect detection analysis and the like on the image; and the control unit is used for controlling the working state of the multi-mode imaging unit and forming a control instruction according to a detection and analysis result. According to the system, the purpose of seamless integration of defect information of multiple procedures across multiple paths and branches is achieved, so that the defects of the absorptive article can be detected more accurately and comprehensively, and products with the defects can be better removed in the follow-up process.

The invention relates to a multi-modal microscopic imaging system and method, and the system comprises a multi-modal imaging module which is used for transmitting a multi-modal laser beam; the scanning system comprises a scanning device and an objective lens; the controller is used for controlling the scanning device to move to a specified pose and controlling the scanning device to conduct the multi-mode laser beam to a target area of the sample to be detected through the objective lens for scanning under the specified pose; wherein a multi-mode optical signal and an ultrasonic signal are returned after surface excitation of the to-be-detected sample; the transducer assembly is positioned between the sample to be detected and the objective lens and is used for collecting ultrasonic signals; and the controller is also used for processing the collected multi-mode optical signal and ultrasonic signal returned by the multi-mode imaging module to generate a scanning result image. According to the invention, the problem of time-space consistency caused by time-sharing detection in traditional multi-mode microscopic imaging is solved.

The invention relates to the technical field of ultrasonic and molybdenum target detection, in particular to an intelligent breast lump diagnosis system based on ultrasonic-molybdenum target multi-modal image and serum P16 protein detection, and the intelligent breast lump diagnosis system is characterized in that an ultrasonic image, a molybdenum target image and multi-modal data of serum P16 protein detection are fused; and optimal integration of cross-modal features is realized through a dynamic weight strategy. The ultrasonic image provides morphological and hemodynamic information of the lumps; the molybdenum target image reveals the characteristics of calcification density, calcification cluster heterogeneity, structure distortion depth and the like; serum P16 protein detection prompts malignant risks from the molecular level, and especially has important value in early recognition of triple negative breast cancer. A feature association graph is constructed through a graph neural network (GNN), the weight is dynamically adjusted in combination with a Bayesian probability integration formula, and optimal fusion of different modal features is ensured. The limitation of a single mode is made up, and the sensitivity and the specificity of diagnosis are also remarkably improved.

Systems and methods are provided for real-time multimodal deformable image registration for image-guided interventions. A pre-interventional three-dimensional (3D) magnetic resonance imaging (MRI) image and multiple 3D ultrasound (US) images capturing various respiratory states and poses are acquired for a patient. The MRI image is registered to each US image using a trained MR-US deformation model, producing deformed MRI images. During intervention, an interventional 3D US image is acquired and registered to a pre-interventional US image using a trained US-US deformation model, determining a warp field. This warp field is applied to the corresponding deformed MRI image, producing a registered 3D MRI image for visualizing annotated tissue features from the pre-interventional MRI on the live interventional US image. The disclosed approach leverages multimodal imaging and deep learning models to enhance visualization during interventions by combining superior soft tissue contrast of MRI with real-time US imaging capabilities.

The invention relates to the technical field of semiconductor defect detection, and discloses a chip defect detection method, device and equipment based on multi-modal optical imaging.The multi-modal imaging data of a chip are obtained through a plurality of optical detection channels, a digital twin is constructed based on the data, a plurality of detection reproduction models are generated, and the detection reproduction models are used for detecting defects of the chip. The method comprises the steps of calculating the information confidence coefficient of each model, converting the information confidence coefficient into a detection feature matrix, calculating the correlation between the models through cross-modal correlation analysis, carrying out abnormal labeling on the feature matrix according to correlation information, carrying out defect perception and generating a detection result. And particularly, the method has high application value in identification of tiny and potential defects, and solves the problem of low detection precision of a single optical imaging technology in the prior art.

The invention relates to the technical field of cardiovascular diseases, in particular to a method for verifying an in-vivo targeted delivery function of a reagent based on an atherosclerosis model. The method comprises the following steps: acquiring lesion characteristic data of an atherosclerosis site, wherein lesion characteristics comprise a lesion position, endothelial permeability, an inflammation state and a vulnerability index; preparing targeted nano microbubbles according to the lesion characteristic data, intravenously injecting the targeted nano microbubbles into a preset atherosclerosis model body, and performing multi-modal image tracking analysis to obtain in-vivo circulation distribution data of the microbubbles. Targeted nano microbubbles are customized and designed based on plaque feature data, the targeting specificity of inflammatory components such as macrophages is remarkably improved by utilizing a double-targeting ligand modification technology, and the problem of low recognition efficiency of a traditional single targeting system is solved.

The invention belongs to the technical field of medical equipment, and provides a tumor radiotherapy system based on multi-modal image and breathing waveform fusion, which comprises a data acquisition and processing module, a tumor position prediction module and a radiotherapy determination module, based on an elastic registration method, through feature space alignment, the space-time registration problem of a multi-source image is effectively solved, particularly artifact suppression and small tumor recognition of a rib overlapping region are innovatively optimized, and the target region sketching precision in a complex anatomical environment is remarkably improved; by means of a two-way convolution module, a prediction model with respiratory phase sensing capacity is constructed by analyzing space-time correlation between respiratory signals and image features, tumor motion trail tracking is achieved, tumor centroid coordinates are predicted, and the technical limitation of traditional single-mode tracking is broken through.

The invention provides a multi-modal image registration method and system based on a deformation field, a storage medium and equipment, and the method comprises the steps: obtaining multi-modal images, including a 2D ultrasonic image and a 3D reconstruction image, and carrying out the coarse registration; obtaining a 2D ultrasonic image sequence of continuous frames, and obtaining rigid change parameters of the organ in a respiratory cycle; acquiring organ contour feature data points of each frame of 2D ultrasonic image, and performing elastic registration to obtain a 2D elastic deformation field of the organ; constructing a 2D-3D deformation function of the organ, and obtaining a 3D deformation field in a three-dimensional space; and updating vertex coordinates of the target organ in the 3D reconstruction image according to the 3D deformation field to obtain a mapped three-dimensional image to represent rigid and elastic changes of the organ. According to the method, by fusing the multi-modal image information and combining rigid and elastic deformation analysis, accurate registration of the ultrasonic image and the CT / MRI image is achieved, and the precision of multi-modal medical image registration is effectively improved.

This invention discloses a multimodal imaging and multidimensional index evaluation software and hardware system for scars, involving an infrared, RGB, and stereoscopic three-modal imaging hardware system, and an intelligent calculation and monitoring software system for more than twenty scar evaluation indicators. The intelligent evaluation system for scars includes indicators such as perimeter, length, width, thickness, area, volume, color, roughness, consistency, elasticity, blood flow density, risk of continued scar growth, and color difference. This forms an objective, multidimensional, precise, and intelligent evaluation system for the scar diagnosis and treatment process, thereby assisting in diagnosis and treatment, as well as monitoring and tracking the entire life cycle of scars. This contributes to the visualization, objectification, and accuracy of the scar diagnosis and treatment process, providing doctors and patients with comprehensive diagnostic and treatment evidence and enabling tracking and tracing.

This disclosure is directed toward systems and methods for monitoring patients in a clinical setting by using non-contact multimodal imaging. For example, based on receiving imaging data, a computing device may identify an object and a region of focus formed by the object. The computing device may then cause a sensor to capture first sensor data at a first time and second sensor data at a second time, wherein the sensor data is associated with the region of focus. Based on determining that a difference between the first sensor data and the second sensor data is greater than or equal to a threshold difference, the computing device may generate and send, to an additional computing device, an alert indicative of the second sensor data.

The present specification relates to the technical field of multimodal imaging and provides a scanning control method, device, and system for photoacoustic and fluorescence dual-modal imaging. The system comprises: a laser; a first data acquisition card for acquiring imaging data of a target in a fluorescence mode; a second data acquisition card for acquiring imaging data of the target in a photoacoustic mode; the first data acquisition card and the second data acquisition card taking the same amount of time to complete single-frame acquisition; and a programmable logic device for, after confirming that the first data acquisition card is in a ready state, synchronously outputting a trigger signal to the first data acquisition card and the second data acquisition card when a specific edge of a laser pulse output by the laser arrives, so that the first data acquisition card and the second data acquisition card operate synchronously.

The invention discloses a self-adaptive fingerprint identification method and module based on multi-modal imaging, and the method comprises the steps: obtaining a multi-spectral fingerprint image sequence, triggering a plurality of light sources in a time-sharing manner, and collecting a spectrum corresponding to each light source; acquiring a stain type and a regional mask image, inputting the multispectral fingerprint image sequence into a target light convolutional neural network model, and performing stain type identification by the target light convolutional neural network model according to the multispectral fingerprint image sequence; and performing adaptive fingerprint identification, performing image enhancement according to the stain type to obtain a first enhanced image, performing local dynamic exposure compensation on the first enhanced image to obtain a second enhanced image, and transmitting the second enhanced image to a fingerprint identification unit for identification. The fingerprint recognition accuracy is improved, and the problems that the effect is poor when a traditional enhanced image software algorithm is used for recognition, and the fingerprint recognition accuracy is low due to the fact that dynamic stain type judgment is not combined are solved.

The application relates to a method for optimizing multimodal imaging and a multimodal imaging system. Based on the multimodal imaging system, an optical coherence imaging module converts collected optical coherence imaging signals into interference photoelectric signals, an optical phase extraction module calculates a wavefront phase diagram according to the interference photoelectric signals, and an adaptive signal processing module generates a driving signal according to the wavefront phase diagram, so that a spatial light modulator in an adaptive optical module is dynamically controlled, the spatial light modulator dynamically adjusts the wavefront phase of incident laser, higher-precision wavefront distortion compensation is realized, and the imaging quality of the multimodal imaging is optimized. The method utilizes the deep penetration and layer cutting capabilities of the optical coherence imaging mode, simultaneously has full field of view and high-speed regulation and control capabilities, accurately adjusts the phase, and realizes adaptive optimization. The method provided by the application improves the overall imaging effect of the multimodal imaging, and promotes the wide application of the multimodal imaging system in the fields of disease detection and scientific research.

The invention discloses a robotic arm surgical traction fixing system combined with a drag hook module, and particularly relates to the field of surgical tractor operation control and state comparison unit analysis. Forming and outputting target state data of the current operation stage in combination with drag hook traction path parameters solved according to the target tissue area data; and the state acquisition module is in structural connection and signal connection with the traction unit through the drag hook module, the mechanical arm unit and the traction unit which are included in the state acquisition module. By constructing a cooperative working mechanism of the target state establishing module and the state obtaining module, real-time matching judgment between the actual combination state of the drag hook module and the operation target state is achieved, and the problem that whether the drag hook module is matched with the operation space requirement logic or not cannot be dynamically sensed in an existing system is solved.

A portable, modular, handheld imaging system is disclosed. The modular system includes a first housing portion and a second housing portion. The first housing portion includes at least one excitation light source. A first filter is configured to detect and allow selected optical signals to pass through a first image sensor in response to illumination with the excitation light. A second filter is configured to detect and allow selected optical signals to pass through a second image sensor in response to illumination of a target surface with white light. The second housing portion is configured to releasably receive the first housing portion. The second housing portion includes a display and a processor configured to receive the detected fluorescent and white light optical signals and output a representation of the target surface to the display based on the detected optical signals.

The present invention relates to the field of artificial intelligence technology, and discloses an intelligent quantitative assessment system for adolescent scoliosis that integrates multimodal imaging. The system includes a multimodal data management module, a cross-modal registration fusion module, a dynamic quantitative analysis module, a risk prediction module, a three-dimensional reconstruction module, a report generation module, and a system management module. By setting a multimodal registration verification mechanism, the accurate matching of bony structure and soft tissue is ensured, and the calculation error of key parameters caused by registration failure is reduced, so that the three-dimensional reconstruction accuracy is greatly improved compared with traditional single-modal assessment. By introducing a dynamic breathing compensation module, the physiological changes of the spine in the respiratory movement cycle are automatically captured during the quantification stage of spinal deformation parameters, and the measurement values ​​are corrected based on an individualized dynamic model to distinguish between pathological progression and physiological fluctuations, thereby avoiding the risk of misjudgment caused by ignoring respiratory phase changes in traditional static measurements, and improving the reliability of scoliosis progression risk assessment.

The application relates to an ophthalmic multimodal imaging joint calibration device and a calibration method. The device comprises a variable diaphragm, an eye simulation lens, a water room and a calibration plate. The eye simulation lens is placed at the best working distance of the calibrated equipment. The variable diaphragm is placed in front of the front surface of the eye simulation lens, and the light aperture of the variable diaphragm can be adjusted at will. The calibration plate is placed at the back focal plane of the eye simulation lens, and a calibration pattern is drawn on the front surface of the calibration plate. The front surface of the calibration plate, the rear surface of the eye simulation lens and the water room form a closed space, and the inside can be injected with liquid. The application is mainly used for joint imaging calibration of fundus camera equipment and ophthalmic OCT equipment, and solves the problem of poor spatial matching of imaging data when two modal instruments are used for joint imaging.