1439 results about "Anatomical structures" patented technology

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 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 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.

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 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 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 technical field of ultrasonic detection, and discloses an ultrasonic scanning path optimization and three-dimensional reconstruction method, device, equipment and medium, and the method comprises the steps: positioning a key mark point of a target area, generating an initial scanning path, and controlling an ultrasonic probe to move along the initial scanning path to collect first ultrasonic image data, extracting target structure features to construct an initial three-dimensional point cloud model; establishing a surface projection model; fitting to generate a surface profile curve and calculating the tangential direction of a sampling point; optimizing the orientation of an ultrasonic probe based on the tangential direction to generate an optimized scanning path; and reconstructing a final three-dimensional model. According to the invention, through key mark point positioning and incident direction optimization based on the tangential direction, the scanning path of the probe is enabled to be adaptive to curvature change of the target area, and through combination of real-time contact force adjustment and multi-stage image data acquisition, three-dimensional point cloud space coverage and imaging consistency are improved, and high-precision three-dimensional reconstruction of a complex anatomical structure is realized.

The invention relates to the technical field of medical image processing and computer vision, in particular to a medical image segmentation method and system based on deep learning, the method is based on a U-shaped encoder-decoder architecture, a DSAB module is introduced into an encoder, and context perception of a directional anatomical structure is enhanced through complementary directional space shift and CSA mechanism weighting; an MGCF module is designed in a decoder, and a parallel multi-scale convolution path and an AGCA mechanism are combined, so that multi-level features are efficiently fused to recover boundary details. Meanwhile, links of data preprocessing, Transform structure details, segmentation result post-processing and the like are supplemented, the model performance is improved through a mixed loss function and an optimization training strategy, and the method has remarkable advantages in segmentation precision and boundary definition and provides powerful support for clinical auxiliary diagnosis.

The invention relates to the technical field of intelligent medical teaching, and discloses a medical intelligent teaching model construction method based on an ultrasonic AI technology. According to the method, ultrasonic image sequence data and corresponding operation records of a target object are integrated to generate an original teaching data set; carrying out multi-dimensional teaching feature analysis on the dynamic teaching feature parameter set, and extracting a dynamic teaching feature parameter set; constructing an anatomical structure evolution feature tensor according to a time evolution rule of the parameter set, and calculating knowledge density distribution of historical typical cases; setting a teaching anomaly discrimination boundary and generating an anomaly feature index set; capturing ultrasonic image flow and operation behavior data in teaching operation in real time, mapping the ultrasonic image flow and operation behavior data to a multi-scale teaching knowledge space, and calculating spatial distribution similarity with the abnormal feature index set to obtain a real-time teaching deviation coefficient; and constructing a teaching risk prediction network model, and generating a teaching operation quality evaluation result and a teaching strategy adjustment scheme.

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 relates to a disease diagnosis and treatment method, system and device based on ear-nose-throat endoscope images and a medium, and the method comprises the steps: synchronously collecting dual-spectrum images through time sequence triggering, and solving the problem of shielding of an anatomical structure caused by mucus flow; a dynamic mucus displacement field is modeled through pixel gradient, and misjudgment of a traditional segmentation method on static lesions and dynamic secretions is eliminated; a deformable convolutional layer is adopted to correct the spatial offset of white light and a narrow-band image, and the mismatch of a multi-mode characteristic due to optical scattering is overcome; and finally, a real-time surgical navigation mark and a clinical treatment scheme are synchronously generated based on topological attributes of the focus probability graph, and a closed-loop link from image analysis to diagnosis and treatment decision is realized. According to the method, the functions of mucus interference suppression, cross-modal accurate registration and real-time diagnosis and treatment assistance are integrated in a breakthrough manner, and the focus recognition accuracy and clinical operation efficiency of the endoscope image are remarkably improved.

The invention discloses an orbit MRI image analysis method and system based on deep learning, and belongs to the field of image analys.The method comprises the steps that MRI image data of an orbit area of a patient are obtained, and the MRI image data comprise a conventional T1WI sequence, a T2WI plain scanning sequence, a conventional enhancement sequence and an extraocular muscle fibrillation enhancement sequence; performing preprocessing on the MRI image to obtain a standardized image; inputting the standardized image into a deep learning segmentation model, and outputting segmentation masks of extraocular muscle, lacrimal gland and intraorbital fat; calculating quantitative indexes of a target structure based on the segmentation mask, wherein the target structure comprises extraocular muscle, lacrimal gland and intraorbital fat; and generating a diagnosis report containing the quantitative index. Human experience dependence is avoided, and objective and uniform anatomical structure segmentation results are ensured.

The invention provides an image segmentation method, a gestational week prediction method and a diagnosis report determination method and device, and belongs to the field of medical artificial intelligence. The image segmentation method comprises the steps that a U-shaped image segmentation network is constructed, and the U-shaped image segmentation network comprises an encoder and a decoder; wherein the encoder and the decoder are connected through a jump connection layer; a self-adaptive feature gating module of each jump connection layer embedded layer of the U-shaped image segmentation network is used for strengthening the expression ability of a key anatomical structure and filtering irrelevant information; a bottleneck layer of the U-shaped image segmentation network is embedded into a bottleneck domain adaptive feature calibration module which is used for guiding deep semantic positioning by utilizing shallow space details and improving multi-section organ segmentation robustness; training the U-shaped image segmentation network by adopting the training set to obtain an image segmentation model; and inputting a to-be-segmented image into the image segmentation model to obtain an image segmentation result. According to the method, standardized, multi-section and multi-structure collaborative segmentation of the image can be realized.

The invention discloses a knee bone joint state recognition method and system based on artificial intelligence, and relates to the technical field of image analysis, and the method comprises the steps: obtaining multi-source medical image data of a knee joint region of a patient, carrying out the spatial registration and standardization preprocessing, and constructing a three-dimensional structure model of a knee joint after unifying a coordinate system; establishing a mapping relation between joint movement and an anatomical structure by combining knee vibration and behavior characteristic data collected by a patient in a specific movement state, extracting joint key geometric and mechanical parameters from the three-dimensional structure model, and fusing the joint key geometric and mechanical parameters with synchronously collected vibration behavior characteristics; and performing state recognition on the fused multi-source features based on an intelligent recognition model, and outputting a health state label of the knee joint. According to the invention, more accurate and more comprehensive knee joint health state identification can be realized, the accuracy and reliability of an identification result are effectively improved, and the timeliness of disease discovery and the scientificity of intervention are enhanced.

The invention discloses a cerebral hemorrhage image recognition method, and aims to solve the problems that different cerebral hemorrhage focuses have significant differences in form, size and position, and cerebral anatomical structures of different individuals also have significant differences, so that the features of cerebral hemorrhage images are complex and changeable, and the recognition accuracy is high. Therefore, a traditional image processing method is difficult to accurately identify the key information of the focus in the cerebral hemorrhage image. The method comprises the steps of obtaining a cerebral hemorrhage image; the cerebral hemorrhage image is input into the trained cerebral hemorrhage image recognition model to obtain a recognition result output by the model, and the recognition result comprises the lesion type, position and size of the lesion in the cerebral hemorrhage image; wherein the cerebral hemorrhage image recognition model is obtained by training an enhanced cerebral hemorrhage image sample obtained by performing elastic deformation processing on a cerebral hemorrhage image sample. The invention further discloses a cerebral hemorrhage image recognition device, electronic equipment, a computer readable storage medium and a computer program product.

The invention discloses a thoracoscope minimally invasive cardiac surgery navigation system based on multi-modal image fusion, and the system comprises the steps: constructing a 5D heart digital twin containing an anatomical structure, a soft tissue boundary, metabolic activity and electrophysiological information through fusing preoperative CT, MRI, PET and intraoperative ultrasonic images; real-time dynamic registration is realized by using an electrocardio gating driven biomechanical model; superposing the multi-color coding navigation information to the visual field of the thoracoscope through a wavelength selection type spectroscope; a dynamic risk scoring model is constructed based on the distance, the speed and the tissue vulnerability, and three-level grading intervention is triggered; a miniature force feedback actuator is integrated to realize closed-loop control; and pre-operation virtual drilling and post-operation federal learning optimization are supported. The system remarkably improves the surgical precision and safety, and is suitable for high-difficulty heart minimally invasive surgeries such as mitral valve repair and atrial fibrillation ablation.

The invention relates to the technical field of medical image processing, and discloses a zero-sample cross-domain diffusion segmentation method based on anatomical structure probability transmission guidance, and the method comprises the steps: constructing a double-flow collaborative learning framework, extracting multi-scale features through a shared structure encoder, learning unconditional anatomical prior through an anatomical decoder, and carrying out the segmentation of the non-conditional anatomical prior. And the image appearance decoder strengthens mode-independent structure perception and realizes appearance and structure decoupling. In the inference stage, a hierarchical probability structure transmission (H-PST) mechanism is introduced, multi-scale features are extracted from an unknown target domain image and a mask in the generation process, the structure distribution difference is measured by using a slice Wasserstein distance, reverse diffusion is dynamically guided by using gradient as a structure transmission force, and general anatomical prior is refined into a segmentation mask aligned with the target image. According to the method, cross-modal / equipment zero sample segmentation can be realized without target domain data, the accuracy, the structural fidelity, the robustness and the interpretability are improved, and the clinical deployment cost is reduced.

The invention discloses a semi-supervised medical image segmentation method based on causal uncertainty decomposition, and belongs to the field of medical image processing and artificial intelligence. According to the method, a segmentation model of teacher and student architectures is constructed, and a causal uncertainty decomposition module, a self-adaptive consistency learning module and a topology perception consistency loss module are integrated. The total uncertainty is decomposed into cognitive uncertainty and random uncertainty, so that targeted processing is realized; a dual-path weight fusion and differential modulation strategy is adopted to realize pixel-level adaptive learning; and a Betti number is introduced to calculate a topological distance, so that the integrity of an anatomical structure is kept. According to the method, under the condition that only 5%-20% of annotation data is used, the Dice coefficient on multiple medical image data sets is increased by 3.2%-4.8%, the segmentation precision and the boundary positioning accuracy are remarkably improved, the segmentation problem under the condition that medical image annotation is scarce is effectively solved, and the method has important clinical application value.

The invention relates to the technical field of medical instruments, and discloses a breast cancer sentinel lymph node positioning system and method, and the system comprises a data collection subsystem, a space positioning subsystem and a central processing subsystem. The method comprises the following steps: acquiring an anatomical structure image, a radioactivity counting rate and fluorescence intensity after performing primary fluorescence, radioactivity and secondary pulse type fluorescence tracer injection in sequence; establishing a global coordinate system and generating an anatomical region of interest; constructing a weighted directed topological graph based on the initial fluorescence data, and obtaining a baseline flow velocity; extracting radionuclide dynamic characteristics based on the radioactive data; a functional flow rate attenuation coefficient is calculated based on the second pulsed fluorescence data. And a multi-factor scoring model is applied, topological information, dynamic characteristics and functional flow velocity attenuation coefficients are fused, and a comprehensive score is calculated to identify the sentinel lymph node. According to the method, the lymphatic flow topological structure, nuclide dynamics and functional flow velocity information are combined, and the positioning accuracy and reliability are improved.

The invention relates to the technical field of deep learning, in particular to a radiotherapy plan dose distribution verification method based on deep learning, and the method comprises the following steps: collecting historical radiotherapy plan data, generating a physical reference dose field through a Monte Carlo algorithm, unifying the voxel resolution of an anatomical structure to 1 cubic millimeter, and normalizing the dose according to a prescription, data enhancement is carried out only by adopting translation and mirror transformation, trace Gaussian noise is added, and a physical information enhanced three-dimensional training data set is constructed. According to the method, a three-dimensional convolutional network is utilized to automatically learn a dose distribution rule of a historical high-quality plan, a physical constraint module is embedded to ensure that a prediction result accords with a radiology principle, a real-time clinical rule engine is combined to instantly identify and correct a violation hot spot cold region, and an uncertainty quantification technology is assisted to position a high-risk region, so that the accuracy of a prediction result is improved. Finally, minute-level full-automatic verification is achieved, executable optimization suggestions are output, and efficiency is improved by dozens of times while safety is improved.

The invention discloses a transcranial stimulation magnetic therapy coil navigation method, a transcranial stimulation magnetic therapy coil navigation system, a storage medium and equipment. The method comprises the following steps: acquiring three-dimensional structure information of the head of a target object, magnetic resonance imaging (MRI) data of an individual target object, or three-dimensional model data obtained by matching from a general MRI template library; real-time surface geometric information of the head of the target object is collected through an infrared optical tracking system; performing registration fusion on the three-dimensional structure information and the real-time surface geometric information, and generating an individualized three-dimensional brain model synchronized with a real space coordinate system on a navigation interface; the spatial position and posture of the optical marker fixed on the magnetic therapy coil are tracked in real time through an infrared optical tracking system and correspondingly mapped to the individualized three-dimensional brain model, and visual navigation of the magnetic therapy coil relative to the brain anatomical structure is achieved. Through the individualized three-dimensional navigation and real-time visualization technology, the problems that a traditional TMS is low in positioning precision, invisible in operation and poor in treatment repeatability are solved.

The invention provides a novel geometric shape quantitative analysis method for brain subregion tissues, which is characterized in that depth feature extraction of a CNN-Transform mixed architecture and quantitative characterization of multi-scale differential geometric features are fused, and comprises the following steps: firstly, constructing an SFUNet dual-path encoder, accurately capturing local details of a brain subregion through depth separable convolution, and extracting the local details of the brain subregion; modeling global semantic association of a cross-brain subregion in combination with a Transform branch, dynamically fusing dual-path features, and accurately segmenting an effective brain subregion tissue range; secondly, designing a quantization method from segmentation to spherical harmonic coefficient (SPHARM) mapping according to the geometric morphology of the curved surface of the brain subregion, namely, extracting Gaussian curvature and average curvature point by point after point-to-point registration through standard spherical parameterization and group average curved surface registration, and respectively representing local convex-concave characteristics and bending strength of a specific brain tissue region; and finally, constructing a multi-scale differential geometric feature vector in combination with an SPHARM low-frequency coefficient, and inputting the multi-scale differential geometric feature vector into an AI algorithm to realize brain tissue collaborative analysis of an anatomical structure and geometric deformation.

Devices, systems, and methods for predicting palatal geometry are provided. In some embodiments, a system for predicting an anatomy of a patient's palate includes one or more processors and a memory operably coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the system to perform operations including accessing a first digital representation including an initial geometry of soft tissue corresponding to a patient's palate, determining an initial geometry of hard tissue of the patient's palate, predicting a change in the hard tissue during a treatment, using the predicted change in the hard tissue to predict a change in the soft tissue during the treatment, and outputting a second digital representation including a predicted geometry of the patient's palate at a future treatment stage of a treatment plan, based on the predicted change in the soft tissue.

The present disclosure relates to surgical systems, devices and methods for planning and performing surgical procedures, including bone fracture repair. The systems and methods disclosed herein can be utilized to establish physical models of anatomical structures.

The invention provides a brain deep electrical stimulation electrode parameter determination method and related equipment, and the method comprises the steps: building a head simulation model which comprises conductivity distribution; determining one or more target stimulation target spots and one or more non-target inhibition areas on the head simulation model; based on one or more target stimulation target spots and one or more non-target inhibition areas, multiple groups of electrode configuration schemes are determined through an electrode positioning method, each group of electrode configuration scheme is configured on the head simulation model, and multiple groups of stimulation simulation models are obtained; performing finite element solution based on the anatomical structure and the conductivity distribution of each group of stimulation simulation models, and optimizing the electric field intensity of one or more target stimulation target spots and the electric field intensity of one or more non-target suppression regions to obtain a plurality of electrode parameters; and determining a target electrode parameter in the plurality of electrode parameters, and taking an electrode configuration scheme corresponding to the target electrode parameter as a target electrode configuration scheme.

The invention discloses a postpartum pelvic floor function evaluation system based on three-dimensional image reconstruction, which belongs to the technical field of medical image processing and biomechanical analysis and comprises an ultrasonic image acquisition module, a three-dimensional reconstruction module, a tissue elasticity analysis module, a rehabilitation scheme generation module and a visual display module. Performing three-dimensional space registration and feature point extraction through a multi-angle ultrasonic image sequence, and realizing three-dimensional reconstruction of a pelvic floor anatomical structure with the precision of 0.2 mm by using a multi-scale fusion network; a deep learning neural network is adopted to extract tissue features, and elastic modulus distribution is calculated through a strain analysis model; an individualized rehabilitation training plan is automatically generated according to an evaluation result, and the rehabilitation effect is improved by 75%; according to the postpartum pelvic floor function evaluation system and method, the three-dimensional visual display and rehabilitation process tracking functions are provided, accurate evaluation and scientific rehabilitation guidance of the postpartum pelvic floor function are achieved, the screening efficiency is improved by 320%, and the clinical application value is remarkable.

The invention relates to the technical field of image recognition, in particular to a medical image intelligent evaluation system based on image recognition. The system comprises an image registration module, an image segmentation module, a preliminary fusion module, an image evaluation module, an optimization feedback module and an image output module. According to the method, the CT image and the MRI image are subjected to image registration, spatial alignment is ensured, then the region of interest is segmented and fused, namely, the skeleton contour in the CT image is superposed on the MRI image, and due to the fact that motion artifacts generated by movement of a patient in the scanning process possibly exist in the original CT image, the skeleton contour in the CT image is fused with the motion artifacts in the MRI image. If the skeleton contour does not exist in the MRI image, the overlapping degree and the blank degree of the skeleton contour and the anatomical structure edge of the MRI image are analyzed, and an optimized registration parameter or segmentation parameter is fed back, so that when the segmentation network is trained, the segmentation precision under the conditions of artifacts and low contrast is improved, spectrum and texture information of the two images is reserved to the maximum extent, and the fusion effect is guaranteed.

The invention provides a tumor cell assisted positioning system and method based on a composite image, and relates to the technical field of medical image.The system comprises a data acquisition module used for synchronously acquiring radio frequency echo signals, ultrasonic images and computed tomography data of target biological tissue to generate an original fusion data set; the feature processing module is used for performing spatial registration and feature extraction on the original fusion data set to obtain a multi-dimensional feature set; and the identification imaging module is used for extracting a multi-band radio frequency echo signal intensity level value of the target area based on the multi-dimensional feature set, generating a tissue radio frequency characteristic diagram through logarithmic compression, and performing pixel-level fusion on the tissue radio frequency characteristic diagram and the synchronous ultrasonic image to generate a composite image. According to the method, the three-dimensional coordinates of the tumor boundary are determined through multi-modal data fusion and precise calibration in combination with anatomical structure constraints, and the accuracy of tumor cell positioning is improved.

A system and computer-implemented method for manufacturing an orthopedic implant involves analyzing tissue characteristics based on image data of anatomy. Image data of a patient can be analyzed to identify at least one tissue characteristic at different locations along anatomic elements of anatomy of interest. A patient-specific implant configuration can be determined based on the analysis of the image data of a patient.

The invention relates to an AI image navigation method, device and equipment for prostatic hyperplasia minimally invasive surgery and a medium thereof. The method comprises the following steps: acquiring a prostate three-dimensional image and an intraoperative real-time image sequence, and fusing the three-dimensional image after preliminary filtering to obtain an optimized and enhanced image; based on the enhanced image and the preoperative three-dimensional image, a three-dimensional offset vector is obtained through a multi-modal registration algorithm combined with an anatomical structure reference, and optimized prostate three-dimensional attitude data is generated in combination with anatomical constraints; combining the attitude data and the three-dimensional image, determining boundary area distribution characteristics, calculating a risk assessment value, mapping a risk level, and generating a path correction instruction to obtain an instrument navigation path if the risk level exceeds a threshold value; and based on the navigation path, constructing a clinical recovery model in combination with the anatomical structure, generating postoperative recovery index simulation data, and performing adjustment and verification until preset requirements are met to obtain a final operation execution scheme. According to the method, precision and individuation of surgical navigation are achieved, and comprehensive technical support is provided for precise implementation of minimally invasive surgery.