63 results about "Tissue deformation" patented technology

The invention relates to the field of medical robots, and discloses a meridian point foundation construction method for precise positioning of an acupuncture robot, which comprises the following steps: scanning skin texture, muscle contour and skeleton mark features of a human body surface, and establishing a human body surface feature digital model; the method comprises the following steps: acquiring temperature distribution data of a human body surface, human body surface feature digital model data and elastic modulus data, performing weight mapping feature level fusion to generate an enhanced acupuncture point feature map, and establishing an acupuncture point-meridian association relationship matrix based on the enhanced acupuncture point feature map; compared with the prior art, the method has the advantages that three-dimensional body surface scanning, infrared thermal imaging and elastic modulus data are integrated through the multi-modal data fusion technology to generate the enhanced acupoint feature map, the dynamic meridian-acupoint twinborn model is constructed in combination with double-ellipse section fitting and implicit curved surface reconstruction, and tissue deformation is simulated in real time.

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 relates to an operation auxiliary system and method based on image processing, and the method comprises the steps: obtaining a preoperative medical image of a patient, collecting a multispectral imaging medical image of a surface structure, blood flow distribution and tissue elasticity of an operation region in real time, carrying out the image feature extraction after preprocessing, obtaining a preoperative image feature and an intraoperative multispectral image feature, and carrying out the image feature extraction; constructing a biomechanical organ model, updating tissue deformation parameters of the biomechanical organ model, performing space-time alignment on the intraoperative multispectral image features and the biomechanical organ model, generating a three-dimensional enhancement model including vascular topology, lesion boundary and elastic distribution, and superposing the three-dimensional enhancement model to a real-time surgical field image to obtain a real-time surgical field image. Marking the relative position of the surgical instrument and the risk area, generating an intervention signal when the instrument is close to a critical safety distance threshold value, and performing surgical operation by a doctor under assistance. Multi-dimensional intraoperative information is obtained through multi-spectral imaging to construct a high-precision dynamic biomechanical model, and precise navigation and active intervention are achieved in combination with the augmented reality technology.

The invention belongs to the technical field of ablation puncture navigation, and particularly relates to an ablation puncture surgery navigation system under AI visual guidance. Through cooperative work of the phase monitoring module, the channel construction module, the needle moving control module, the displacement prediction module and the feedback control module, an intelligent surgical navigation system is constructed, breathing phase information and multi-modal medical image data of a patient can be obtained in real time, and therefore a safe and optimized needle moving path is accurately planned, and the operation accuracy is improved. Meanwhile, through real-time monitoring and quantitative evaluation of parameters such as tissue deformation and pressure distribution in the needle moving process, potential operation risks can be found and corrected in time, smooth operation is ensured, in addition, the system can dynamically adjust the needle moving path and the puncture angle according to the real-time breathing state of a patient, and the operation safety is improved. The accuracy and the safety of the operation are further improved, so that intelligent management and personalized treatment in the ablation puncture operation process are realized.

The invention provides a control method of an intestinal tumor resection robot combining preoperative images and intraoperative navigation, and relates to the technical field of medical instrument control. The method comprises the following steps: acquiring preoperative multi-modal image data of the intestinal tract of a patient, and performing three-dimensional reconstruction on the image data to generate an initial three-dimensional model; dividing a navigation area in the enteric cavity according to the initial three-dimensional model; based on the core area, collecting a dynamic image of the intestinal tract inner cavity in real time through intraoperative navigation equipment, extracting anatomical mark points in the dynamic image, registering the dynamic image with the initial three-dimensional model, updating tissue deformation parameters in the three-dimensional model, and obtaining an updated three-dimensional model; on the basis of the updated three-dimensional model, obstacles in the intestinal tract are recognized, and the minimum space distance between the obstacles and the robot tail end instrument is calculated; and according to the spatial distribution and the minimum spatial distance of the obstacles, an initial motion path of the robot tail end instrument is planned. According to the invention, the efficiency and accuracy of the intestinal tumor resection operation can be improved.

The invention provides a pelvic floor muscle function exercise method and system based on remote digital guidance, and relates to the technical field of remote rehabilitation guidance, and the method comprises the steps: arranging a multi-channel micro sensor array at a preset position of a pelvic floor muscle group, and collecting electromyographic signals and tissue deformation data in resting and contraction states; generating a biomechanical model through Fourier transform and multi-dimensional mapping calculation; performing convolutional neural network analysis on the model and a standard physiological database to generate functional damage positioning data; the remote medical data center calculates a neural pathway activation parameter based on the injury positioning data, generates a training instruction and converts the training instruction into a biological feedback control signal; collecting real-time biomechanical data in the training process, calculating a muscle group synergy efficiency value and generating a fatigue characteristic curve; training parameters are adjusted in real time through wavelet transformation and deep reinforcement learning network analysis, a training scheme iteration closed loop is formed, and precise remote guidance of pelvic floor functional rehabilitation is achieved.

The invention discloses an intraoperative pulmonary nodule positioning method and system based on fusion of a CT image and an endoscope video. The method comprises the following steps: firstly, constructing a three-dimensional lung model containing a pulmonary nodule position based on preoperative high-resolution CT data; generating a real-time lung surface model by using an endoscope video stream through a three-dimensional reconstruction technology during the operation; a feature point extraction and matching algorithm is adopted, and a non-rigid registration technology is combined to realize accurate registration of the preoperative model and the intraoperative dynamic model; projection display of pulmonary nodules in a real-time endoscope visual field is established through space coordinate mapping, and a dynamic compensation algorithm is developed to solve the problem of tissue deformation caused by respiratory movement and instrument operation. Meanwhile, the virtual nodule position is overlaid to an endoscope video picture through the augmented reality technology, and visual intraoperative navigation is provided for surgeons. The method effectively solves the technical bottleneck that a traditional method depends on a metal marker and cannot adapt to intraoperative tissue deformation and the like, and has clinical practical value.

The invention belongs to the technical field of biomedical engineering, and discloses a preoperative human body intelligent positioning system for medical images, which comprises a data standardization unit for setting a sampling frequency based on a preset respiratory gating signal, collecting multi-modal image data of a patient according to the sampling frequency, and storing the multi-modal image data in a database; processing by using a point location matching method to obtain standardized multi-modal image data; the deformation tracking unit is used for carrying out difference operation and squaring on gray values of different position points in the standardized multi-mode image data to obtain different error squares; combining all error squares to obtain an error square matrix, and generating an error distribution thermodynamic diagram based on the error square matrix; inferring tissue deformation through an error distribution thermodynamic diagram, and updating tissue deformation field parameters by adopting an optimization algorithm; the accuracy and individuation level of preoperative planning are improved, the dynamic assistance and real-time compensation capability can be provided in the operation, and finally high-precision, low-risk and intelligent human body preoperative positioning and navigation are achieved.

The invention provides a percutaneous puncture accurate dynamic matching and navigation method and system based on an AI surgical robot, and the method comprises the steps: obtaining a preoperative medical image, and generating surgical planning data based on the medical image; determining an ablation path and an ablation area according to the surgical planning data; puncture operation is executed through the robot system, and ablation treatment of the target area is completed; the step S3 further comprises the steps of obtaining real-time image data in the operation for multiple times, and dynamically adjusting the ablation path and the ablation parameters in real time based on the real-time image data in the operation each time. The method comprises the following steps: acquiring and analyzing preoperative and intraoperative medical image data of a patient to generate puncture positioning and navigation puncture schemes in different stages; the purpose of capturing tissue deformation in real time in percutaneous puncture is achieved, the purpose of dynamically optimizing the puncture path is further achieved based on the tissue deformation state captured in real time, the motion trail of the robot is adjusted in real time based on real-time generation of the dynamic puncture path, and therefore the precise navigation and dynamic matching degree of the percutaneous puncture operation is improved, and the accuracy of the percutaneous puncture operation is improved. And the purpose of precise puncture is achieved.

The invention relates to the technical field of medical image processing, and discloses a multi-mode ultrasonic mammary gland and multi-mode fusion method. The method comprises the following steps: S1, acquiring medical images of at least two modalities of a mammary gland area, wherein the medical images comprise a first modal image and a second modal image; s2, performing standardization preprocessing on the first modal image and the second modal image; and S3, constructing a physical modeling model of the breast tissue based on the second modal image, and generating a tissue deformation displacement field to obtain tissue response information. By adopting a multi-modal ultrasonic mammary gland image registration and fusion technology and combining feature fusion and deformation field optimization based on an image neural network, the image registration precision is remarkably improved. Compared with a traditional image registration method in the prior art, the multi-modal image registration method not only optimizes the alignment effect among the multi-modal images, but also effectively reduces registration errors caused by modal differences, and particularly provides more accurate alignment on detail structures of breast tissues.

The invention relates to the technical field of medical robots, in particular to a laser treatment auxiliary robot and an auxiliary method thereof, and the method comprises the following steps: scanning a motion area, and obtaining obstacle detection records by calculating point cloud density, boundary form and curvature change parameters and identifying obstacles in the motion area. According to the method, through multi-layer point cloud scanning, the recognition precision of the mechanical arm on the obstacles in the movement area is optimized, the space sensing capacity is improved, the real-time data of the joint torque sensor are combined, the accuracy of collision risk assessment is enhanced, the speed and torque are dynamically adjusted, and the movement safety and stability are improved; according to the method, treatment starting coordinates are extracted by adopting tissue deformation in unit time, a surface curvature change value and a boundary shortest distance, the matching degree of a treatment area is improved, laser treatment is more accurate, a motion path is optimized by combining a surface geometrical shape and real-time operation feedback, trajectory mutation is reduced, and safety and consistency of a treatment process are improved.

The embodiment of the invention provides a method and equipment for dynamically adjusting a motion control mode based on an intraoperative visual field environment, a medium and a program product, and relates to the field of intelligent medical treatment. The method comprises the following steps: acquiring an intraoperative view image and a preset basic force threshold value according to the tissue type of a to-be-operated site; calculating an intraoperative tissue real-time deformation rate based on the intraoperative view image, and calculating a real-time force safety threshold according to a preset basic force threshold and the tissue real-time deformation rate; receiving real-time contact force of the instrument and the tissue, comparing the real-time contact force with a real-time force safety threshold value, and if the real-time contact force is smaller than or equal to the real-time force safety threshold value, outputting an instruction of damping force in direct proportion to the real-time contact force; if the real-time contact force is larger than the real-time force safety threshold value, an emergency braking program instruction is output. According to the method, factors, such as tissue deformation and illumination change, which cause instability of the surgical field of view in the surgical environment are introduced, so that the environmental adaptability and dynamic adjustment of the field of view mechanism in the robot-assisted surgical operation process are improved.

The invention relates to the technical field of remote single-hole robot operation guidance and demonstration, and discloses a system and a method for remote single-hole robot operation guidance and demonstration, which realize efficient, stable and secure transmission of remote force feedback signals and control instructions through a low-delay secure communication link based on SRv6 + TSN and a national password SM9 encryption technology. The problem that transparency and real-time performance are difficult to consider in remote operation is effectively solved, experts can seamlessly perceive interaction force of local instruments and tissues and carry out accurate remote intervention, and through dynamic modeling of a cloud twinning service module and Transformer and graph neural network algorithms, the interaction force between the local instruments and the tissues can be accurately and remotely intervened. Real-time prediction of risks such as bleeding and perforation in the operation and accurate matching of tissue deformation are achieved, a visual and fused AR guidance interface is provided for experts in combination with a mixed reality guidance engine, and the accuracy of remote guidance and operation safety are remarkably improved.

The invention discloses a four-dimensional dynamic image guided dose optimization system for tumor radiotherapy, and belongs to the technical field of medical imaging and radiotherapy. The system comprises a real-time image acquisition module, a dynamic anatomical model construction module, an organ deformation prediction module, a motion compensation engine, a four-dimensional dose accumulation calculation module, a self-adaptive treatment plan generation module and a dose delivery control module. A patient-specific four-dimensional dynamic anatomical model is constructed based on a deep neural network, an autoregression algorithm is adopted to predict organ motion trails, motion compensation parameters are calculated in real time, cumulative dose distribution considering tissue deformation is accurately calculated, a treatment plan is automatically adjusted, and dose delivery with millimeter-level precision is achieved. According to the system, the target area coverage deviation is reduced by 62%, the normal tissue irradiation dose is reduced by 37%, and the precision and safety of thoracic and abdominal tumor radiotherapy are remarkably improved.

The invention discloses a sequence image relative pose estimation method, device and equipment and a medium, and relates to the technical field of image processing, the method accurately estimates inter-frame poses through deep learning, so that ultrasonic three-dimensional reconstruction can be realized under the condition of not using external tracking equipment such as electromagnetic tracking and the like; therefore, various possible complex medical environments can be adapted more flexibly, and medical workers can be helped to perform examination and diagnosis better. And meanwhile, the method eliminates the dependence of ultrasonic three-dimensional reconstruction on external equipment such as optical / magnetic tracking equipment, so that the system architecture is simpler and more compact, the hardware cost and complexity are remarkably reduced, and the method is favorable for adapting to various different clinical application scenes. Besides, through rotation isovariant convolution and multi-scale cross attention, the method can better adapt to factors such as angle change and tissue deformation in the acquisition process, and the robustness of the method is improved.

This invention relates to the field of biomedical technology, and more particularly to a real-time lesion AR annotation method for endoscopic systems based on augmented reality. The method first acquires and preprocesses a real-time video stream from the endoscope; then, a two-stage cascaded model is used to detect and segment lesions in the preprocessed video stream, identifying suspicious lesion areas, generating a segmentation mask, and removing interfering areas in the endoscopic scene; real-time pose data of the endoscope is acquired using an electromagnetic-visual fusion positioning method; based on the segmentation mask and real-time pose data, two-dimensional lesion information is mapped to three-dimensional space to construct a three-dimensional lesion model, which is then completed by combining anatomical structural features and adjusted according to tissue deformation; finally, the adjusted three-dimensional lesion model is back-projected onto the endoscopic field of view to generate AR annotation information, which is then fused with the real-time endoscopic image and output. This invention enables real-time and accurate annotation of endoscopic lesions, providing auxiliary support for endoscopic diagnosis and treatment.

There is provided an accelerated coupled filtering method for tissue deformation analysis. The method includes initializing, by a first processing device, a plurality of first motion parameters; applying, by a second processing device, a first filter on a pre-deformation image of a tissue to obtain a filtered pre-deformation image, wherein the second processing device is different from the first processing device; applying, by the second processing device, a second filter on a post-deformation image of the tissue to obtain a filtered post-deformation image, wherein the filtered pre-deformation image and the filtered post-deformation image are correlated by a first motion matrix comprising the plurality of first motion parameters, and wherein the plurality of first motion parameters comprise at least three fundamental first motion parameters, each of the three fundamental first motion parameters represents movement of the tissue along an axial direction relative to the axial direction, an elevational direction and a lateral direction, respectively; estimating, by the second processing device, a respective value for each of the plurality of first motion parameters, wherein each of the estimated respective values represents a difference between the pre-deformation and post-deformation filtered images; and in response to determining that at least one of the estimated respective values meets at least one predefined criterion, updating the at least one of the estimated respective values as an optimal value for at least one corresponding first motion parameter of the plurality of first motion parameters.

A surgical robotic system for automatic port repositioning includes a robotic arm coupled to an access port inserted through an incision, the access port is equipped with sensors to detect tissue deformation. Using imaging or force sensors, the system monitors tension around the port and calculates motion vectors to perform micro-adjustments, or "port burping," to relieve strain without disrupting surgery. The robotic arm executes these adjustments gradually, ensuring smooth operation. Additionally, the system monitors task and workspace volumes to keep instruments within safe zones, using algorithms like SLAM and bump detection. If instruments approach workspace limits, the system autonomously adjusts the port's position to expand the task space, minimizing manual intervention and enhancing surgical efficiency.

The embodiment of the invention provides a dynamic tissue deformation-oriented real-time electrical impedance tomography optimization method and system. The method comprises the following steps of: obtaining dynamic deformation characteristics of a target tissue; based on the dynamic deformation characteristics, determining and adjusting a data sampling frequency; acquiring electrical impedance data by using the adjusted data sampling frequency; and performing real-time image reconstruction based on the electrical impedance data. Through the scheme of the embodiment of the invention, the problem of how to regulate and control the data sampling frequency according to the dynamic tissue deformation characteristics so as to solve the problem of insufficient real-time performance of image reconstruction can be solved.

The invention discloses a feedback force prediction method and device in a virtual operation process, and belongs to the technical field of feedback force prediction in the virtual operation process, and the feedback force prediction method comprises the steps: obtaining data needed by prediction and data needed by updating in response to collision detected in the virtual operation process; based on the data required for updating, a lightweight online learning strategy is adopted to dynamically update the trained TCN-Transform parallel enhanced time sequence fusion model; inputting data required by prediction into the model to obtain a tissue deformation prediction value and a feedback force prediction value; calculating a tissue deformation prediction error based on the tissue deformation prediction value and the tissue deformation calculation value; and if the tissue deformation prediction error is smaller than a preset decision threshold, taking the feedback force prediction value as a final prediction result. According to the method, feedback force output delay is remarkably reduced through prediction instead of calculation, sudden operation changes are adapted through dynamic updating, and prediction accuracy is maintained.

This invention provides an intraoperative residual organ volume estimation system for surgical planning assistance, relating to the field of minimally invasive surgery. By matching the preoperative and intraoperative tissue mesh models, this invention transforms the measurement of residual organ volume into a volume measurement of the corresponding region in the preoperative tissue mesh model, avoiding interference from complex tissue deformation and invisible areas on volume prediction. Furthermore, it considers intraoperative interaction with the surgeon, receiving simple annotations from the surgeon regarding regions of interest to obtain accurate volume measurement information, achieving active selectivity and high reference value during surgery. In addition, the introduced online self-supervised learning depth estimation method based on binocular endoscopy utilizes a binocular depth estimation network with rapid overlearning capabilities, continuously adapting to new scenarios using self-supervised information, thereby ensuring the accuracy of the intraoperative tissue mesh model.

Embodiments of the present application provide a result simulation method and device based on a generative adversarial network, and an equipment, and relate to the field of image processing. The method comprises: inputting a CT image and a proton beam flow parameter into a trained generative adversarial network; after the CT image is encoded and decoded by a primary generator, a basic tissue deformation field is obtained; after the basic tissue deformation field and a Bragg peak distribution diagram obtained based on the proton beam flow parameter are subjected to feature fusion and attention weight mapping by a fine-tuning generator, an enhanced image and an output feature vector are obtained; after the output feature vector is subjected to spatial feature discrimination in a double-channel discriminator, an image authenticity result is obtained; after the output feature vector is subjected to dosimetric feature discrimination in the double-channel discriminator, a dosimetric evaluation result is obtained; and after the enhanced image is discriminated according to the image authenticity result and the dosimetric evaluation result, an output image of the trained generative adversarial network is obtained. The embodiments provided in the present application improve the prediction accuracy.

The invention discloses an endoscope monocular dense reconstruction and real-time positioning method based on deformation modeling. The method comprises the following steps: constructing a hybrid map model combining a sparse key point graph and three-dimensional Gaussian point representation; performing initial three-dimensional structure reconstruction and non-rigid camera attitude estimation through the sparse point map; fine pose adjustment and high-quality reconstruction are carried out on the Gaussian map in combination with micro-rendering optimization; efficient conversion from sparse geometry to dense geometry is realized through a sparse prior guided depth alignment mechanism, and tracking stability and reconstruction density are both considered. According to the method, three-dimensional mapping and tracking of the deformation scene can be realized in real time under the monocular camera, the method is suitable for an endoscopic surgery scene with complex tissue deformation, and the visualization quality and the navigation precision in the surgery are improved.

The utility model discloses an anti-tissue-deformation antibacterial impedance cardiogram electrode slice, which relates to the technical field of medical instruments and comprises a metal connecting buckle, a composite hydrogel layer is adhered to the bottom of the metal connecting buckle, an electric conductor is arranged at the edge of the bottom of the metal connecting buckle, and the metal connecting buckle is connected with the composite hydrogel layer. The upper surface of the bottom of the metal connecting buckle is bonded with a release film, the edge of the release film is provided with a fabric electrode, the composite hydrogel layer is composed of TCP and iodine nanosheets, the iodine nanosheets are uniformly embedded in the TCP, and the size of a single iodine nanosheet is at the nanoscale. The antibacterial impedance cardiogram electrode plate capable of resisting tissue deformation is wide in antibacterial spectrum, high in antibacterial capacity, capable of being repeatedly used, capable of reducing resource waste, high in tissue viscosity and shaping capacity, capable of solving the problem that the electrode plate is prone to falling off in the impedance cardiogram detection process, high in tissue compatibility, high in reliability and good in anti-bacterial effect. The problem of medical viscose-related skin injury of part of examinees is solved.

This invention provides a method and system for soft tissue collaborative surgery control based on a humanoid robot. It relates to the fields of medical robotics, computer vision, and remote teleoperation. The method includes: displaying, via a display terminal, image stream information acquired in real time by the humanoid robot for the surgical operation area, wherein the surgical operation area includes the area where a first instrument held by a first robotic hand of the humanoid robot is located; in response to detecting a grasping operation representing soft tissue grasped by the first instrument, obtaining the tissue deformation rate of the grasped soft tissue; generating, based on the tissue deformation rate of the grasped soft tissue, a prompt message for indicating the tissue stretching state on the display terminal; and sending the prompt message to the display terminal for display.

A method for determining personal protective equipment (PPE) comfort for an individual wearer includes defining a first anatomical shape data representative of an anatomical area of the individual wearer prior to donning a PPE, a second anatomical shape data representative of the anatomical area of the individual wearer after donning the PPE, and comparing the first anatomical shape data with the second anatomical shape data. The method further includes determining a soft skin tissue deformation at a plurality of predetermined anatomical positions based on the comparison between the first anatomical shape data and the second anatomical shape data, and determining a displacement comfort threshold (CTd) value based on the soft skin tissue deformation. The method also includes determining a pressure pain threshold (PPT) value, and determining a comfort metric based on the PPT values and the CTd values. The method also includes generating a notification corresponding to the comfort metric.

The invention relates to the technical field of wireless communication network resource management, in particular to an interactive resource allocation method for a virtual anatomical model in a wireless network, which comprises the following steps of: 1, acquiring virtual organization deformation data caused by user operation in real time, and dynamically dividing operation precision grades based on the deformation degree; 2, generating a dynamic space influence range according to the operation precision grade, detecting a space overlapping region of multi-user operation, and calculating a conflict risk value; 3, dynamically calculating the resource allocation quantity by combining the operation precision grade, the organization deformation data and the conflict risk value; and 4, triggering resource redistribution when tissue deformation suddenly changes or an operation tool is replaced. Through accurate operation precision division and dynamic adjustment of resource allocation, resource waste can be effectively avoided in a multi-user interaction environment, meanwhile, real-time response of key operation is ensured, and the misoperation rate caused by delay or resource insufficiency is greatly reduced.

This invention discloses an intelligent puncture path planning method for interventional surgical robots, belonging to the field of interventional robot path planning technology. The method acquires multimodal medical images of the patient's puncture site, completes organ tissue segmentation and three-dimensional reconstruction, generates a digital twin anatomical model, and calibrates basic puncture parameters; constructs a respiratory phase tracking model, combines the basic puncture parameters to lock the respiratory cycle and real-time respiratory phase, and outputs the three-dimensional predicted coordinates of the puncture target point for a pre-set duration; acquires needle insertion force data through a six-dimensional force sensor, calculates tissue compression deformation and needle body flexible bending offset in real time, and generates a feedforward compensation vector; fuses the three-dimensional predicted coordinates and the feedforward compensation vector to generate a dynamic puncture path, performing continuous needle insertion without interruption throughout the entire process. This invention improves upon the shortcomings of traditional static path planning, which cannot adapt to respiratory displacement and tissue deformation, effectively improving puncture positioning accuracy and is suitable for minimally invasive interventional puncture surgery scenarios.

The utility model relates to the technical field of embedding boxes, and discloses an embedding box convenient for lumen tissue sampling, which comprises a box body assembly, a lumen splint and a box cover, the box cover is arranged on the box body assembly in an openable and closable manner, the lumen splint is arranged inside the box body assembly, and the lumen splint is used for clamping and fixing the lumen tissue; the lumen clamping plate comprises a baffle, sleeves and springs, a plurality of clamping grooves are formed in the inner side of the baffle at intervals, the sleeves are fixed in the clamping grooves, the clamping grooves and the sleeves are semicircular, and every two opposite sleeves are spliced into a circular sleeve used for clamping and fixing lumen tissue; a plurality of springs are arranged between the outer sides of the baffles and the inner wall of the box body assembly and between every two adjacent baffles. The box cover comprises a cover plate, through box cover through holes are evenly formed in the cover plate, and a plurality of supporting columns are fixed to the top end of the cover plate. According to the utility model, vertical and uniform pressure can be applied to lumen-like tissues, so that tissue deformation is reduced, the lumen structure can be better distinguished, and pathological diagnosis can be better carried out.

This invention discloses a method and apparatus for predicting feedback force during virtual surgery, belonging to the technical field of feedback force prediction during virtual surgery. The feedback force prediction method includes: in response to collision detection during virtual surgery, acquiring data required for prediction and data required for updating; based on the data required for updating, dynamically updating a trained TCN-Transformer parallel enhanced temporal fusion model using a lightweight online learning strategy; inputting the data required for prediction into the model to obtain predicted tissue deformation values ​​and predicted feedback force values; calculating the tissue deformation prediction error based on the predicted tissue deformation values ​​and the calculated tissue deformation values; if the tissue deformation prediction error is less than a preset decision threshold, then the predicted feedback force value is used as the final prediction result. This invention significantly reduces the feedback force output delay by replacing calculation with prediction, and maintains prediction accuracy by dynamically updating to adapt to sudden operational changes.